Redshift and Databricks provide the volume, speed, and quality demanded by business intelligence (BI) applications. But there are as many similarities as there are differences between these two data leaders. Therefore, selection often boils down to platform preference and suitability for your organization’s data strategy:

• Databricks: Best for real-time data processing and machine learning capabilities.
• AWS Redshift: Best for large-scale data warehousing and easy integration with other AWS services.

Databricks vs. Redshift: Comparison Chart

Databricks Overview

Databricks is a unified analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data and machine learning projects. It is built on top of Apache Spark, an open-source data processing engine, and offers several tools and services to simplify and accelerate the development of data-driven applications.

Databricks is well-suited to streaming, machine learning, artificial intelligence, and data science workloads — courtesy of its Spark engine, which enables use of multiple languages. It isn’t a data warehouse: Its data platform is wider in scope with better capabilities than Redshift for ELT, data science, and machine learning. Users store data in managed object storage of their choice and don’t get involved in its pricing. The platform focuses on data lake features and data processing. It is squarely aimed at data scientists and highly capable analysts.

Databricks Key Features

Databricks lives in the cloud and is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework, which makes management of infrastructure easier. Some of Databricks’ defining features include:

Auto-Scaling and Auto-Termination

Databricks automatically scales clusters up or down based on workload demands, optimizing resource usage and cost efficiency. It can also terminate clusters when they are no longer needed, reducing idle costs. This feature is particularly beneficial for companies with fluctuating workloads or those looking to optimize cloud costs.

MLflow

Databricks MLflow simplifies the machine learning lifecycle by providing tools to manage the end-to-end ML process—from experimentation to production deployment and monitoring. Data science teams in various industries benefit from MLflow for reproducibility, collaboration, and operationalizing machine learning models.

Delta Lake

Databricks Delta Lake provides reliable data lakes with ACID transactions and scalable metadata handling. It allows for more efficient data management and streamlines data engineering workflows. Companies dealing with large-scale data processing and analytics, especially those with real-time data needs, find Delta Lake valuable. It’s often used in industries like finance, healthcare, and retail.

Databricks Pros and Cons

Databricks offers some great strengths, including its ability to handle huge volumes of raw data, and and its multicloud approach – the platform interoperates with the leading cloud providers. However, a challenge for some: the platform is geared for advanced users; many use cases require real expertise.

Pros

• Databricks uses a batch in-stream data processing engine for distribution across multiple nodes.
• As a data lake, Databricks’ emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.
• The platform can be used for raw unprocessed data in large volumes.
• Databricks is delivered as software as a service (SaaS) and can run on AWS, Azure, and Google Cloud.
• There is a data plane as well as a control plane for back-end services that delivers instant compute.
• Databricks’ query engine is said to offer high performance via a caching layer.
• Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

Cons

• Some users, though, report that it can appear complex and not user-friendly, as it is aimed at a technical market and needs more manual input for resizing clusters or configuration updates.
• There may be a steep learning curve for some.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It allows users to analyze large amounts of data using SQL queries and BI tools to gain insights. Major AWS users would be best on Redshift due to better integration with the entire Amazon ecosystem.

AWS Redshift Key Features

Redshift positions itself as a petabyte-scale data warehouse service that can be used by BI tools for analysis. Some of its best features include:

Columnar Storage and Massively Parallel Processing

Amazon Redshift uses columnar storage and MPP architecture to deliver high performance for complex queries on large datasets. It’s optimized for analytics workloads. Redshift is designed for scalability and performance, making it suitable for enterprises processing terabytes to petabytes of data.

Integration with AWS Ecosystem

Redshift seamlessly integrates with other AWS services like S3, Glue, and IAM, simplifying data ingestion, transformation, and security management within the AWS cloud. Companies heavily invested in the AWS ecosystem and those looking for a fully managed data warehousing solution often choose Redshift.

Concurrency Scaling

Redshift’s concurrency scaling functionality automatically adds and removes query processing power in response to the workload, ensuring consistently fast query performance even during peak usage. This capability is essential for businesses with unpredictable query patterns or those needing consistent performance under heavy loads, such as during business intelligence reporting.

AWS Redshift Pros and Cons

Redshift certainly benefits from being a product of the powerful AWS platform – it offers enormous scalability, and provides a long list of services. However, in some instances it can be expensive, and it doesn’t support all types of semi-structured data.

Pros

• Redshift scales up and down easily.
• Amazon offers independent clusters for load balancing to enhance performance.
• Redshift offers good query performance — courtesy of high-bandwidth connections, proximity to users due to the many Amazon data centers around the world, and tailored communication protocols.
• Amazon provides many services that enable easy access to reliable backups for Redshift datasets.

Cons

• Some users noted that Redshift can sometimes be complex to set up and use at times and ties up more IT time on maintenance due to lack of automation.
• A lack of flexibility in areas, such as resizing, can lead to extra expense and long hours of maintenance.
• It lacks support for some semi-structured data types.

Databricks vs. Redshift: Support and Ease of Implementation

Databricks offers an array of support of advanced use cases, while Redshift tends to be more user friendly.

Databricks

Databricks offers a variety of support options that can be used for technical and developer use cases:

• Databricks can run Python, Spark Scholar, SQL, NC SQL, and other platforms.
• It comes with its own user interface as well as ways to connect to endpoints, such as Java database connectivity (JDBC) connectors.

Redshift

Amazon Redshift is said to be user-friendly and demands little administration for everyday use:

• Setup, integration, and query running are easy for those already storing data on Amazon S3.
• Redshift supports multiple data output formats, including JSON.
• Those with a background in SQL will find it easy to harness PostgreSQL to work with data.

Support and Implementation Winner: Redshift

This category is close, although Redshift is the narrow winner. The platform benefits from its support by AWS. The platform offers relatively accessible ease of implementation.

Databricks vs. Redshift: Integration

Databricks in some cases calls for third party solutions to integrate certain tools, while Redshift is of course a top choice for existing AWS customers.

Databricks

Databricks requires some third-party tools and application programming interface (API) configurations to integrate governance and data lineage features. Databricks supports any format of data, including unstructured data. But it lacks the vendor partnership depth and breadth that Amazon can muster.

Redshift

Obviously, those already committed to the AWS platforms will find integration seamless on Redshift with services like Athena, DMS, DynamoDB, and CloudWatch. The level of integration within AWS is excellent.

Integration Winner: It Depends

Redshift wins in this category, if a company is an AWS client. Obviously, the fact that Redshift is an integral part of the AWS platform helps in this category. In contrast, Databricks integrates with all the major cloud providers (including AWS, of course) and is used by multicloud clients – it clearly is not AWS-dependent.

Databricks vs. Redshift: Pricing

Pricing can vary considerably based on use case: Databricks can be pricey for users who require consultant help, and Redshift charges by the second if daily allotment is exceeded. This category is practically a toss-up.

Databricks

Databricks takes a different approach to packaging its services. Compute pricing for Databricks is tiered and charged per unit of processing, with its lowest paid tier starting at $99 per month. However, there is a free version for those who want to test it out before upgrading to a paid plan.

Databricks may work out cheaper for some users, depending on the way the storage is used and the frequency of use. For example, consultant fees for those needing help are said to be expensive.

Redshift

Redshift provides a dedicated amount of daily concurrency scaling. But you get charged by the second if it is exceeded. Customers can be charged an hourly rate by type and cluster nodes or by amount of byte scanning. That said, Redshift’s long-term contracts come with big discounts.

Roughly speaking, Redshift has a low cost per hour. But the rate of usage will vary tremendously depending on the workload. Some users say Redshift is less expensive for on-demand pricing and that large datasets cost more.

Pricing Winner: Redshift

This is a close one, as it varies from use case to use case, but Amazon Redshift gets the nod.

The differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and analysis requirements before making a purchasing decision.

Databricks vs. Redshift: Security

Like pricing, this category is a close call. Both platform are focused on security.

Databricks

Databricks provides role-based access control (RBAC), automatic encryption, and plenty of other advanced security features. These features include network controls, governance, auditing and customer-managed keys. The company’s serverless compute deployments are protected by multiple layers of security.

Redshift

Redshift does a solid job with security and compliance. These features are enforced comprehensively for all users.

Additionally, tools are available for access management, cluster encryption, security groups for clusters, data encryption in transit and at rest, SSL connection security, and sign-in credential security. These tools enable security teams to monitor network access and traffic for any irregularities that might indicate a breach.

Access rights are granular and can be localized. Thus, Redshift makes it easy to restrict inbound or outbound access to clusters. The network can also be isolated within a virtual private cloud (VPC) and linked to the IT infrastructure via a virtual private network (VPN).

Security Winner: Tie

Both platforms do a good job of security, with strong compliance and monitoring tools, so there is no clear winner in this category.

Who Shouldn’t Use Databricks or AWS Redshift?

Who Shouldn’t Use Databricks 

• Small businesses with minimal data needs: For small businesses with relatively simple data processing and analysis requirements, Databricks may be overly complex and expensive.
• Companies not leveraging cloud platforms: Databricks is tightly integrated with major cloud platforms like AWS, Azure, and GCP. If an organization prefers on-premises solutions or has strict data residency requirements that limit cloud adoption, Databricks may not be the best fit.
• Limited use cases: If the primary focus is on traditional data warehousing and analytics without extensive machine learning or data engineering needs, simpler tools like traditional SQL-based data warehouses might be more suitable.

Who Shouldn’t Use Redshift

• Non-AWS cloud users: Although Redshift is tightly integrated with AWS services, organizations using other cloud providers like Azure or Google Cloud Platform might face challenges in terms of interoperability and data transfer costs when considering Redshift.
• Small-scale or start-up companies: Redshift, being a powerful data warehousing solution, may not be cost-effective for smaller businesses with limited data volumes and budget constraints.

2 Top Alternatives to Databricks & AWS Redshift

Google Cloud Dataproc

Google Cloud Dataproc is a managed Apache Spark and Hadoop service offered by Google Cloud Platform. Similar to Databricks, it provides a fully managed environment for running Spark and Hadoop jobs. However, unlike Databricks, Google Cloud Dataproc supports a broader range of open-source big data tools beyond Spark, such as Hadoop, Hive, and Pig.

Snowflake

Snowflake is a cloud-based data warehouse solution that offers similar capabilities to Redshift. It is known for its simplicity, scalability, and separation of storage and compute. Snowflake automatically handles infrastructure management, scaling, and performance optimization, making it easier to use compared to Redshift.

How We Evaluated Databricks vs. AWS Redshift

To write this review, we evaluated each tool’s key capabilities across various data points. We compared their features, ease of implementation, support, pricing, and integrations to help you determine which platform is the best option for your business.

Our analysis found that Databricks and Redshift tie for features and security, the integration category is a toss-up, and Redshift tops for ease of implementation and pricing – though pricing can vary of course based on utilization.

Bottom Line: Databricks and AWS Redshift Use Different Approaches 

In summary, Databricks wins for a technical audience, and Amazon wins for a less technically savvy user base. Databricks provides pretty much all of the data management functionality offered by AWS Redshift. But it isn’t as easy to use, has a steep learning curve, and requires plenty of maintenance. Yet it can address a wider set of data workloads and languages. And those familiar with Apache Spark will tend to gravitate towards Databricks.

AWS Redshift is best for users on the AWS platform that just want to deploy a good data warehouse rapidly without bogging down in configurations, data science minutia, or manual setup. It isn’t nearly as high-end as Databricks, which is aimed more at complex data engineering, ETL (extract, transform, and load), data science, and streaming workloads. But Redshift also integrates with various data loading and ETL tools and BI reporting, data mining, and analytics tools. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and more will certainly make it attractive to developers in those camps.

The post Databricks vs. Redshift: Data Platform Comparison appeared first on eWEEK.

• Azure Synapse: Best for unified data analytics across big data systems and data warehouses.
• Databricks: Best for use cases such as streaming, machine learning, and data science-based analytics.

Continue reading to see how Azure Synapse and Databricks stack up against each other in terms of pricing, features, implementation, AI, security, and integration.

Azure Synapse vs. Databricks at a Glance

Azure Synapse Overview

Azure Synapse, previously known as Microsoft Azure SQL Data Warehouse, integrates big data and data warehousing into a single platform.

Its architecture is built on a strong SQL foundation, designed to handle large volumes of data through massively parallel processing. This approach allows Synapse to deliver rapid processing without solely relying on expensive memory, utilizing clustered and nonclustered column store indexes to efficiently manage data storage and distribution.

Key Features

• Limitless scale and query flexibility: Azure Synapse can handle massive datasets without compromising performance, as users can query data across various sources, including data warehouses, data lakes, and big data analytics systems, using both relational and nonrelational data in their preferred language. This feature is particularly beneficial for organizations with diverse data ecosystems as they likely need seamless integration and analysis of all data types.

• Integrated machine learning and BI: The integration with Power BI and Azure Machine Learning empowers users to discover insights across all data. Practitioners can apply machine learning models directly within their apps, significantly reducing the development time for BI and ML projects. This democratizes advanced analytics and allows users to leverage intelligence across all critical data, including third-party datasets, and enhance decision-making processes.

• Unified analytics workspace: Synapse Studio offers a comprehensive workspace for various data tasks, from data prep and management to data warehousing and artificial intelligence. Its code-free environment for data pipeline management, coupled with automated query optimization and seamless Power BI integration, streamlines project workflows. Teams looking to collaborate efficiently on analytics solutions, from data engineers and scientists to business analysts, will appreciate this capability.

• Real-time insights with Azure Synapse Link: Azure Synapse Link eliminates traditional ETL (extract, transform and load) bottlenecks by providing near-real-time data integration from operational databases and business applications to Azure Synapse Analytics. Organizations can achieve an end-to-end business view more quickly and efficiently, which gives rise to a data-driven culture by democratizing data access across teams.
• Advanced security and privacy: Azure Synapse ensures data protection with state-of-the-art security features, including automated threat detection and always-on encryption. Fine-grained access controls, such as column-level and row-level security, encryption, and dynamic data masking, safeguard sensitive information in real time. This thorough approach to security, backed by Microsoft’s significant investment in cybersecurity, provides peace of mind for organizations concerned with data privacy and compliance.

Pros

• Ideal for analytics with its comprehensive analytics service.
• Offers data protection, access control, and network security features.
• Scalability through massively parallel processing, enabling efficient performance optimization.
• Delivers deep integration with Azure services for enhanced data management and analytics workflows.

Cons

• Can be complex due to its broad range of features.
• Pricing depends on various factors, like the number of data warehouse units and the amount of data stored.
• High-performance configurations can significantly consume resources.
• While powerful within the Azure ecosystem, it may be less flexible outside of it.

Databricks Overview

Databricks, founded on Apache Spark, offers a unified analytics platform that emphasizes machine learning and AI-driven analytics. Positioned more as a data lake than a traditional data warehouse, Databricks excels in handling raw, unprocessed data at scale. Its SaaS delivery model across AWS, Azure, and Google Cloud provides flexibility and scalability to serve a vast range of data processing and analytics needs.

Key Features

• Data Sharing with Delta Sharing: Databricks allows secure data sharing with Delta Sharing, enabling data and AI asset sharing within and outside organizations. This feature is crucial for businesses looking to collaborate on data projects across different platforms, enhancing data accessibility and collaboration.

• Data engineering: Databricks excels in data engineering, offering robust tools for data preprocessing and transformation. This is essential for organizations focusing on developing machine learning models, ensuring data is in the right format and quality for analysis.

• Comprehensive data governance: With features like data cataloging and quality checks, Databricks ensures data is clean, cataloged, and compliant, making it discoverable and usable across the organization. This is vital for companies aiming to maintain high data quality and governance standards.
• Advanced data warehousing: Databricks brings cloud data warehousing capabilities to data lakes with its lakehouse architecture, allowing modeling of a cost-effective data warehouse on the data lake. This suits businesses looking for scalable and efficient data warehousing solutions.
• Artificial intelligence and machine learning: Databricks provides a vast platform for AI and ML, including support for deep learning libraries and large language models. Users can monitor data, features, and AI models in one place, which is useful for organizations looking to leverage AI and ML for advanced analytics and insights.

Pros

• Robust support for machine learning and AI projects with integrated tools like MLflow.
• Built on Apache Spark, ensuring high performance for data processing tasks.
• Available on AWS, Azure, and Google Cloud, providing deployment flexibility.
• Shared notebooks facilitate collaboration and boost productivity of data teams.

Cons

• Aimed at a technical market, it may appear complex and not user-friendly.
• Requires more manual input for tasks like cluster resizing or configuration updates.
• Can be costly for extensive data processing and storage needs.
• Integrating with existing data systems and workflows may need significant effort.

Best for Pricing: Databricks

When comparing the pricing models of Azure Synapse and Databricks, Databricks offers a more accessible entry point with its 14-day free trial, which includes a collaborative environment for data teams and interactive notebooks supporting a wide range of technologies. Its products employ a pay-as-you-go model that ranges between a starting price of $0.07 per Databricks Unit and $0.40 per Databricks Unit.

Azure Synapse, on the other hand, provides a detailed pricing structure that includes options for pre-purchasing Synapse Commit Units (SCUs) for savings over pay-as-you-go prices, with discounts up to 28%.

Pricing for Azure Synapse is based on various factors, including data pipeline activities, integration runtime hours, and data storage, with specific charges for serverless and dedicated consumption models.

While Azure Synapse offers a comprehensive and scalable solution, the complexity of its pricing model and the potential costs associated with large-scale data warehousing and data analytics workloads might make Databricks a more cost-effective option for teams just starting out or with variable usage patterns.

Best for Core Features: Azure Synapse

Azure Synapse offers a comprehensive suite of analytics services that integrate enterprise data warehousing and big data processing. Its core features include limitless scale for querying data, integration with Power BI and Azure Machine Learning for expanded insights, and a unified analytics workspace in Synapse Studio for data prep, management, and exploration.

These capabilities make Azure Synapse particularly well-suited for teams that want a robust platform that can handle extensive data warehousing and analytics tasks within the Azure ecosystem.

Databricks positions itself as more of a data lake than a data warehouse. Thus, the emphasis is more on use cases such as streaming, machine learning, and data science-based analytics. It can be used to handle raw unprocessed data in large volumes.

For those wanting a top-class data warehouse for analytics, Azure Synapse wins. But for those needing more robust ELT (extract, load, transform), data science, and machine learning features, Databricks is the winner.

Best for Ease of Implementation: Azure Synapse

Synapse’s reliance on SQL and Azure offers familiarity to the many companies and developers who use those platforms around the world. For them, it is easy to use. Similarly, Databricks is perfect for those used to Apache tools. But Databricks does take a data science approach, using open-source and machine libraries, which may be challenging for some users.

Databricks can run Python, Spark Scholar, SQL, NC SQL, and other platforms. It comes packaged with its own user interface as well as ways to connect to endpoints such as JDBC connectors. Some users, though, report that it can appear complex and not user friendly as it is aimed at a technical market and needs more manual input for cluster resizing or configuration updates. There may be a steep learning curve for some.

Best for Machine Learning & AI: Databricks

Databricks beats Azure in this category with its Mosaic AI, part of the Databricks Data Intelligence Platform. This platform unifies data, model training, and production environments into a single solution, allowing for the secure use of enterprise data to augment, fine-tune, or build custom machine learning and generative AI models. Databricks offers a more specialized environment tailored for ML and AI development, making it the preferred platform for data scientists and teams working on cutting-edge AI projects.

Azure Synapse Analytics also offers AI and ML capabilities, particularly through its integration with Azure AI services. It allows the enrichment of data with AI in Synapse Analytics using pretrained models from Azure AI services. The platform supports a variety of AI tasks, such as sentiment analysis, anomaly detection, and cognitive services, directly within Synapse notebooks. However, Azure Synapse’s AI and ML functionalities are more about leveraging existing Azure services rather than providing a deeply integrated, customizable ML environment.

Best for Security: Azure Synapse

This is highly dependent on use case; however, for enterprise users, Synapse is the winner. Azure Synapse implements a multilayered security architecture, ensuring end-to-end protection of data. Key security features include data protection with encryption at rest and in motion, comprehensive access control, authentication to verify user and application identities, network security with private endpoints and virtual networks, and advanced threat protection.

This extensive security framework, combined with Azure’s enterprise-grade compliance, makes it quite hard to overlook Azure Synapse as the superior choice for organizations with stringent security and privacy requirements.

Databricks also emphasizes security, offering features like Databricks Runtime for Machine Learning with built-in security for ML workflows, collaborative notebooks with role-based access control, and integration with enterprise security systems. However, Azure Synapse’s deep integration with the broader Azure security and compliance ecosystem, along with its detailed security layers, provides a more holistic security approach.

Best for Integration: Azure Synapse

Azure Synapse offers a wide range of integrations with third-party data integration solutions, supporting a wide corporate ecosystem that includes both Azure and on-premises data sources, as well as legacy systems. This extensive integration capability is facilitated by partnerships with numerous third-party providers such as Ab Initio, Aecorsoft, Alooma, and Alteryx, among others.

Databricks also provides robust integration options, particularly through its Partner Connect hub, which simplifies the integration process with Databricks clusters and SQL warehouses. Databricks supports a variety of data formats like CSV, Delta Lake, JSON, and Parquet, and connects with major data storage providers such as Amazon S3, Google BigQuery, and Snowflake. Additionally, Databricks Repos offers repository-level integration with Git providers, enhancing the development workflow within Databricks notebooks.

However, Azure Synapse’s broader range of data integration partnerships, combined with its native integration within the Azure ecosystem, offers a more extensive solution for organizations seeking to consolidate and analyze data from a wide array of sources.

Who Shouldn’t Use Azure Synapse or Databricks

Even as robust and extensively featured as these two platforms are, it’s impossible to meet all the needs of all kinds of data professionals.

Who Shouldn’t Use Azure Synapse

Azure Synapse, with its expansive data analytics capabilities and integration within the Azure ecosystem, might not be the best fit for small businesses or startups that have limited data analytics requirements or budget constraints. The platform’s complexity and the need for a certain level of technical expertise to navigate its extensive features can and will frustrate organizations that don’t have a dedicated data team.

Additionally, companies not already using Azure services might struggle to integrate Synapse into their existing workflows, making it less ideal for those outside the Azure ecosystem.

Who Shouldn’t Use Databricks

Databricks is tailored for data science and engineering projects. As a result, it can be overwhelming for nontechnical users or those new to data analytics. Its reliance on Apache Spark and emphasis on machine learning and artificial intelligence might not align with the needs of projects that require straightforward data processing or analytics solutions.

Moreover, the cost associated with Databricks’ advanced capabilities, especially for large-scale data processing, might not be justified for organizations with simpler data analytics needs or limited financial resources.

Best Alternatives to Azure Synapse & Databricks

Google Cloud BigQuery

BigQuery, Google’s fully managed enterprise data warehouse, excels in managing and analyzing data with features like machine learning and geospatial analysis. Its serverless architecture allows for SQL queries to answer complex organizational questions without infrastructure management.

BigQuery’s separation of compute and storage layers enables dynamic resource allocation, enhancing performance and scalability. It’s great for teams that want a powerful analytics tool with fast query execution and extensive data integration capabilities.

Snowflake

Snowflake’s cloud data platform is known for its unique architecture that separates compute from storage, allowing for independent scaling and a pay-as-you-go model. It supports standard and extended SQL, transactions, and advanced features like materialized views and lateral views.

Snowflake’s approach to data encryption, object-level access control, and support for PHI data underlines its commitment to security and compliance. It gives organizations a flexible, scalable solution with strong security features.

Teradata Vantage

Teradata Vantage offers a connected multicloud data platform for enterprise analytics, solving complex data challenges efficiently. Vantage is known for its high-performance analytics, comprehensive data integration, and advanced AI and machine learning capabilities, great for enterprises that want reliable analytics across diverse data sets and cloud environments.

Review Methodology: Azure Synapse vs. Databricks

We compared Azure vs. Databricks based on their cost, capabilities, integrations, approach to AI and ML, and user experience.

• Pricing: We evaluated the cost structures of both platforms, considering the transparency and predictability of pricing models, the availability of free trials or versions, and the overall value for money.
• Core features: We examined the capabilities of the two to determine what each is good at. For Azure Synapse, we focused on its data integration, analytics, and management capabilities, while for Databricks, we looked at its collaborative environment, performance optimization, and support for machine learning and AI workflows.
• AI and ML capabilities: We assessed each platform’s strengths in supporting AI and ML projects, such as the availability of built-in models and integration with external AI services.
• User experience: The ease of use, interface design, and ease of setting up are some of the factors we analyzed here to determine which platform provides a more user-friendly experience.
• Integration: We looked at each platform’s ability to integrate with other tools and services, including data sources, BI tools, and other cloud services.

FAQs: Azure Synapse vs. Databricks

What is the difference between Azure Synapse & Databricks?

Azure Synapse integrates data warehousing and big data analytics within the Azure ecosystem, offering a unified analytics workspace. Databricks, based on Apache Spark, focuses on collaborative data science and machine learning, supporting a wide range of data analytics workflows.

How do Azure Synapse & Databricks handle big data processing & analytics differently?

Azure Synapse uses a massively parallel processing architecture ideal for enterprise data warehousing, while Databricks leverages Spark’s in-memory processing for real-time analytics and AI-driven projects, making it suitable for data science tasks.

Are there any specific use cases where Azure Synapse excels over Databricks, & vice versa?

Synapse is preferred for traditional data warehousing and integration within the Azure platform, making it a more fitting choice for businesses that need large-scale data management. On the other hand, Databricks excels in data science and machine learning projects, which make it a better consideration for a more flexible environment for collaborative analytics.

Bottom Line: Azure Synapse vs. Databricks

Azure Synapse and Databricks each cater to different aspects of data analytics and management. Synapse is ideal for enterprises deeply integrated with Microsoft Azure that need robust data warehousing solutions and is more suited for data analysis and for users familiar with SQL.

Databricks is better suited for data science teams requiring a collaborative environment with strong machine learning and AI capabilities and is better suited than Synapse for a technical audience. Ultimately, choosing between the two is based on platform preference, an organization’s use case, existing infrastructure, and the financial resources of an organization.

For a deeper understanding of the data analytics market, see our guide: Best Data Analytics Tools 

The post Azure Synapse vs. Databricks: Data Platform Comparison 2024 appeared first on eWEEK.

We’ll discuss – using X, formerly known as Twitter – the issues and challenges involved with getting the most from your data analytics, a process that offers enormous competitive edge to those who master it.

See below for:

• Participant list for this month’s eWeek Tweetchat on Governing AI
• Questions we’ll discuss in this month’s eWeek Tweetchat
• How to Participate in the Tweetchat
• Tentative Schedule: Upcoming eWeek Tweetchats

Participants List: Data Analytics Best Practices

The list of experts for this month’s Tweetchat currently includes the following – please check back for additional expert guests:

• Andi Mann, Global CTO and Founder, Sageable
• Derek Ashmore, Application Transformation Principal, Asperitas Consulting
• Henrik Wagner, SVP and Global Practice Leader, Lemongrass
• Luis Flynn, Open Source Software & ModelOps, SAS
• Adrian Estala, VP, Chief Field Data Officer, Starburst
• Anil Inamdar, VP Global Services, Instaclustr, NetApp
• Bruce Kornfeld, Chief Marketing and Product Officer, StorMagic
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Editor-in-Chief, eWeek [moderator]

Tweetchat Questions: Data Analytics Best Practices

The questions we’ll tweet about will include the following – check back for more/revised questions:

1. Here in early 2024, what’s the current state of enterprise data analytics? Do most companies have an effective strategy?
2. What key trends are driving the data analytics sector sector?
3. What are the most frustrating data analytics challenges today? Staff training? Data governance?
4. How do you recommend addressing these data analytics challenges?
5. What Best Practices advice would you give to companies to grow their data analytics usage?
6. What about AI and data analytics? Your overall sense of how this combination changes the analytics sector?
7. Let’s look ahead: what enduring challenges will data analytics continue to face in the future?
8. Also about the future: Your best advice to help companies prepare for the future of analytics?
9. A last Big Thought about data analytics – what else should managers/buyers/providers know about gaining advantage from their data?

How to Participate in the Tweetchat

The chat begins promptly at 11 AM PT on February 13th. To participate:

1. Open Twitter in your browser. You’ll use this browser to Tweet your replies to the moderator’s questions.

2. Open Twitter in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by that day’s tweetchat panel of experts.

That’s it — you’re ready to go. Be ready at 11 AM PT to participate in the tweetchat.

NOTE: There is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eWeekchat column.

#eWEEKchat Tentative Schedule for 2024*

January 16: Governing Generative AI
February 13: Data Analytics Best Practices
March 12: AI in the Enterprise: LLMs to Security
April 16: Managing Multicloud Computing
May 14: Optimizing Generative AI
June 11: Mid-Year Look Ahead: Future of Tech

*all topics subjects to change

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The solution, which can run on commodity hardware or in the public cloud, seeks to help enterprises vexed by unstructured data. The company says that Scale Anywhere uses a unified approach to improve efficiency, security, and business agility.

In a briefing with ZK Research, Qumulo CTO Kiran Bhageshpur gave me some background on the platform. “We look at this as being the third era of unstructured data,” he told me. “The first era was NetApp with scale-up, dual controller architectures, and millions of files. It was really a sort of analysis box, if you will. The second era was Isilon, then EMC Isilon, now Dell EMC Isilon, which is scale-out storage, hardware appliances, on-premises, lots of them together to form large single volumes.”

Cloud-Based Qumulo Competes with Legacy Systems

Kiran said that Qumulo started in the cloud computing era, looked at the world, and realized it was no longer the scale-up or scale-out era.

“This is the scale-anywhere era of large-scale data,” he said. “It’s not only lots of data in the enterprise data center—there is incredible growth in the cloud and out at the edge. And Qumulo, with a pure software solution, can now present a solution for all of this data—cloud, on-premises, and the edge in one consistent way.”

Qumulo says that Scale Anywhere introduces a way for enterprises to use on-premises storage in a similar way to cloud storage.

The company jointly developed Azure Native Qumulo (ANQ) with Microsoft. This cloud-native enterprise file system helps eliminate the tradeoffs that often come with balancing scale, economics, and performance.

Qumulo is trumpeting a number of advantages to the approach, including:

• Affordability: Qumulo says that ANQ is about 80% cheaper than competitive offerings and compares well to the costs of traditional on-premises storage.
• Elasticity: Qumulo says that ANQ separates the scalability of capacity and performance so they can operate independently.
• Cloud configurable: Qumulo says enterprises can use the Azure service portal to configure and deploy ANQ quickly.
• Data services: Qumulo says that ANQ provides several data services, including quotas, snapshots, multi-protocol access, enterprise security integrations, and real-time data analytics.

The company also announced Qumulo Global Namespace (Q-GNS), which acts as a unified data plane for unstructured data.

“This is the core feature of the underlying Qumulo file system, and it allows the customer to access remote data on a remote Qumulo cluster as if it were local,” Kiran told me. “Think of two, three, or four Qumulo clusters talking to each other. You can connect to the local one. And as long as it’s configured correctly, you can access data on a Qumulo cluster in the cloud or on-premises halfway across the world, and it feels as though it were local.”

In the announcement, JD Whitlock, CIO of Dayton Children’s Hospital, said that his hospital uses Q-GNS.

“We are rapidly adopting cloud to store our long-term radiology images while keeping new images on-premises,” Whitlock said. “Qumulo’s Global Namespace makes it easy to bring our file-based workloads to the cloud without refactoring any applications.”

Also see: Top Cloud Service Providers and Companies

Bottom Line: Storage for the Cloud Era

Legacy storage vendors like Dell EMC view data storage as an entitlement and haven’t delivered innovation in years. Many believe storage to be a commodity with little room for new features and functions, but that’s not true. The announcement by Qumulo modernizes storage for the cloud era. The company has a lot of work ahead of it, but the approach is innovative and might just make a dent in the defenses of the legacy players.

Read next: Top Digital Transformation Companies

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Both of these application and service suites are in heavy demand, as organizations seek to harness real-time repositories of big data for various enterprise use cases, including artificial intelligence and machine learning model development and deployment.

When choosing between two of the most highly regarded data platforms on the market, users often have difficulty differentiating between Cognos and Power BI and weighing each of the platform’s pros and cons. In this in-depth comparison guide, we’ll compare these two platforms across a variety of qualities and variables to assess where their strengths lie.

But first, here’s a glance at the areas where each tool excels most:

• Cognos Analytics: Best for advanced data analytics and on-premises deployment. Compared to Power BI, Cognos is particularly effective for advanced enterprise data analytics use cases that require more administrative controls over security and governance. Additionally, it is more reliable when it comes to processing large quantities of data quickly and accurately.
• Power BI: Best for affordable, easy-to-use, integrable BI technology in the cloud. Compared to Cognos Analytics, Power BI is much more versatile and will fit into the budget, skill sets, and other requirements of a wider range of teams. Most significant, this platform offers free access versions that are great for teams that are just getting started with this type of technology.

Cognos vs. Power BI at a Glance

What Is Cognos?

Cognos Analytics is a business intelligence suite of solutions from IBM that combines AI-driven assistance, advanced reporting and analytics, and other tools to support various enterprise data management requirements. The platform is available both in the cloud and on demand for on-premises and custom enterprise network configurations.

With its range of features, Cognos enables users to connect, verify, and combine data and offers plenty of dashboard and visualization options. Cognos is particularly good at pulling and analyzing corporate data, providing detailed reports, and assisting in corporate governance. It is built on a strong data science foundation and is supported by heavy-duty analytics and recommendations, courtesy of IBM Watson.

Also see: Top Business Intelligence Software

Key Features of Cognos

• AI-driven insights: The platform benefits from veteran AI support in the form of Watson, which helps with data visualization design, dashboard builds, forecasting, and data explainability. This is particularly helpful for users with limited data science and coding experience who need to pull in-depth analyses from complex datasets.
• Data democratization through natural language: Advanced natural language capabilities make it possible for citizen data scientists and less-experienced tech professionals to create accurate and detailed data visualizations.
• Advanced reporting and dashboarding: Multi-user reports and dashboards, personalized report generation, AI-powered dashboard design, and easy shareability make this a great platform for organizations that require different levels of data visibility and granularity for different stakeholders.
• Automation and governance: Extensive automation and governance capabilities help power users scale their operations without compromising data security. The platform’s robust governance and security features are important to highly regulated businesses and large enterprises in particular.

Pros

• The platform is well integrated with other business tools, like Slack and various email inboxes, making it easier to collaborate and share insights across a team.
• Its AI assistant works well for a variety of data analytics and management tasks, even for users with no data science experience, because of its natural language interface.
• Cognos comes with flexible deployment options, including on-demand cloud, hosted cloud, and client hosting for either on-premises or IaaS infrastructure.

Cons

• The platform is not particularly mobile-friendly compared to similar competitors.
• While a range of visuals are available on the platform, many user reviews indicate that the platform’s visuals are limited and not very customizable.
• Depending on your exact requirements, Cognos Analytics can become quite expensive, especially if you have a high user count or require more advanced features like security and user management.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization software solution that acts as one part of the Microsoft Power Platform. Because of its unification with other Power Platform products like Power Automate, Power Apps, and Power Pages, this BI tool gives users diverse low-code and AI-driven operations for more streamlined data analytics and management. Additional integrations with the likes of Microsoft 365, Teams, Azure, and SharePoint are a major selling point, as many business users are already highly invested in these business applications and are familiar with the Microsoft approach to UX/UI.

Specific to analytics functions, Power BI focuses most heavily on data preparation, data discovery, dashboards, and data visualization. Its core features enable users to take visualizations to the next level and empower them to make data-driven decisions, collaborate on reports, and share insights across popular applications. They can also create and modify data reports and dashboards easily and share them securely across applications.

Key Features of Power BI

• Rapidly expanding AI analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• CRM integration: Power BI integrates relatively well with Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Embedded and integrated analytics: The platform is available in many different formats, including as an embedded analytics product. This makes it possible for users of other Microsoft products to easily incorporate advanced analytics into their other most-used Microsoft products. You can also embed detailed reports in other apps for key stakeholders who need information in a digestible format.
• Comprehensive visualizations: Adjustable dashboards, AI-generated and templated reports, and a variety of self-service features enable users to set up visuals that can be alphanumeric, graphical, or even include geographic regions and maps. Power BI’s many native visualization options mean users won’t have to spend too much time trying to custom-fit their dashboards and reports to their company’s specific needs.

Pros

• Power BI is one of the more mobile-friendly data platforms on the market today.
• In addition to its user-friendly and easy-to-learn interface, Microsoft offers a range of learning resources and is praised for its customer support.
• Its AI-powered capabilities continue to grow, especially through the company’s close partnership with OpenAI.

Cons

• Some users have commented on the tool’s outdated interface and how data updates, especially for large amounts of data, can be slow and buggy.
• The platform, especially the Desktop tool, uses a lot of processing power, which can occasionally lead to slower load times and platform crashes.
• Shareability and collaboration features are incredibly limited outside of its highest paid plan tier.

Best for Core Features: It Depends

It’s a toss-up when it comes to the core features Cognos Analytics and Power BI bring to the table.

Microsoft Power BI’s core features include a capable mobile interface, AI-powered analytics, democratized report-building tools and templates, and intuitive integrations with other Microsoft products.

IBM Cognos Analytics’ core features include a web-based report authoring tool, natural-language and AI-powered analytics, customizable dashboards, and security and access management capabilities. Both tools offer a variety of core features that work to balance robustness and accessibility for analytics tasks.

To truly differentiate itself, Microsoft consistently releases updates to its cloud-based services, with notable updates and feature additions over the past couple of years including AI-infused experiences, smart narratives (NLG), and anomaly detection capabilities. Additionally, a Power BI Premium version enables multi-geography capabilities and the ability to deploy capacity to one of dozens of data centers worldwide.

On the other hand, IBM has done extensive work to update the Cognos home screen, simplifying the user experience and giving it a more modern look and feel. Onboarding for new users has been streamlined with video tutorials and accelerator content organized in an easy-to-consume format. Additionally, improved search capabilities and enhancements to the Cognos AI Assistant and Watson features help generate dashboards automatically, recommend the best visualizations, and suggest questions to ask — via natural language query — to dive deeper into data exploration.

Taking these core capabilities and recent additions into account, which product wins on core features? Well, it depends on the user’s needs. For most users, Power BI is a stronger option for general cloud and mobility features, while Cognos takes the lead on advanced reporting, data governance, and security.

Also see: Top Dashboard Software & Tools

Best for Ease of Use and Implementation: Power BI

Although it’s close, new users of these tools seem to find Power BI a little easier to use and set up than Cognos Analytics.

As the complexity of your requirements rises, though, the Power BI platform grows more difficult to navigate. Users who are familiar with Microsoft tools will be in the best position to use the platform seamlessly, as they can take advantage of skills from applications they already use, such as Microsoft Excel, to move from building to analyzing to presenting with less data preparation. Further, all Power BI users have access to plenty of free learning opportunities that enable them to rapidly start building reports and dashboards.

Cognos, on the other hand, has a more challenging learning curve, but IBM has been working on this, particularly with recent user interface updates, guided UI for dashboard builds, and assistive AI. The tool’s AI-powered and Watson-backed analytics capabilities in particular lower the barrier of entry to employing advanced data science techniques.

The conclusion: Power BI wins on broad usage by a non-technical audience, whereas IBM has the edge with technical users and continues to improve its stance with less-technical users. Overall, Power BI wins in this category due to generally more favorable user reviews and commentary about ease of use.

Also see: Top AI Software

Best for Advanced Analytics Capabilities: Cognos

Cognos Analytics surpasses Power BI for its variety of in-depth and advanced analytics operations.

Cognos integrates nicely with other IBM solutions, like the IBM Cloud Pak for Data platform, which extends the tool’s already robust data analysis and management features. It also brings together a multitude of data sources as well as an AI Assistant tool that can communicate in plain English, sharing fast recommendations that are easy to understand and implement. Additionally, the platform generates an extensive collection of visualizations. This includes geospatial mapping and dashboards that enable the user to drill down, rise, or move horizontally through visuals that are updated in real time.

Recent updates to Cognos’s analytical capabilities include a display of narrative insights in dashboard visualizations to show meaningful aspects of a chart’s data in natural language, the ability to specify the zoom level for dashboard viewing and horizontal scrolling in visualizations, as well as other visualization improvements.

On the modeling side of Cognos, data modules can be dynamically redirected to different data server connections, schemas, or catalogs at run-time. Further, the Convert and Relink options are available for all types of referenced tables, and better web-based modeling has been added.

However, it’s important to note that Cognos still takes a comparatively rigid, templated approach to visualization, which makes custom configurations difficult or even impossible for certain use cases. Additionally, some users say it takes extensive technical aptitude to do more complex analysis.

Power BI’s strength is out-of-the-box analytics that doesn’t require extensive integration or data science smarts. It regularly adds to its feature set. More recently, it has added new features for embedded analytics that enable users to embed an interactive data exploration and report creation experience in applications such as Dynamics 365 and SharePoint.

For modeling, Microsoft has added two new statistical DAX functions, making it possible to simultaneously filter more than one table in a remote source group. It also offers an Optimize ribbon in Power BI Desktop to streamline the process of authoring reports (especially in DirectQuery mode) and more conveniently launch Performance Analyzer to analyze queries and generate report visuals. And while Copilot is still in preview at this time, this tool shows promise for advancing the platform’s advanced analytics capabilities without negatively impacting its ease of use.

In summary, Power BI is good at crunching and analyzing real-time data and continues to grow its capabilities, but Cognos Analytics maintains its edge, especially because Cognos can conduct far deeper analytics explorations on larger amounts of data without as many reported performance issues.

Also see: Data Analytics Trends

Best for Cloud Users: Power BI; Best for On-Prem Users: Cognos

Both platforms offer cloud and on-premises options for users, but each one has a clear niche: Power BI is most successful on the cloud, while Cognos has its roots in on-prem setups.

Power BI has a fully functional SaaS version running in Azure as well as an on-premises version in the form of Power BI Report Server. Power BI Desktop is also offered for free as a standalone personal analysis tool.

Although Power BI does offer on-prem capabilities, power users who are engaged in complex analysis of multiple on-premises data sources typically still need to download Power BI Desktop in addition to working with Power BI Report Server. The on-premises product is incredibly limited when it comes to dashboards, streaming analytics, natural language, and alerting.

Cognos also offers both cloud and on-premises versions, with on-demand, hosted, and flexible on-premises deployment options that support reporting, dashboarding, visualizations, alters and monitoring, AI, and security and user management, regardless of which deployment you choose. However, Cognos’ DNA is rooted in on-prem, so it lags behind Microsoft on cloud-based bells and whistles.

Therefore, Microsoft gets the nod for cloud analytics, and Cognos for on-prem, but both are capable of operating in either format.

Also see: Top Data Visualization Tools

Best for Integrations: It Depends

Both Cognos Analytics and Power BI offer a range of helpful data storage, SaaS, and operational tool integrations that users find helpful. Ultimately, neither tool wins this category because they each have different strengths here.

Microsoft offers an extensive array of integration options natively, as well as APIs and partnerships that help to make Power BI more extensible. Power BI is tightly embedded into much of the Microsoft ecosystem, which makes it ideally suited for current Azure, Dynamics, Microsoft 365, and other Microsoft customers. However, the company is facing some challenges when it comes to integrations beyond this ecosystem, and some user reviews have reflected frustrations with that challenge.

IBM Cognos connects to a large number of data sources, including spreadsheets. It is well integrated into several parts of the vast IBM portfolio. It integrates nicely, for example, with the IBM Cloud Pak for Data platform and more recently has added integration with Jupyter notebooks. This means users can create and upload notebooks into Cognos Analytics and work with Cognos Analytics data in a notebook using Python scripts. The platform also comes with useful third-party integrations and connectors for tools like Slack, which help to extend the tool’s collaborative usage capabilities.

This category is all about which platform and IT ecosystem you live within, so it’s hard to say which tool offers the best integration options for your needs. Those invested in Microsoft will enjoy tight integration within that sphere if they select Power BI. Similarly, those who are committed to all things IBM will enjoy the many ways IBM’s diverse product and service set fit with Cognos.

Also see: Digital Transformation Guide: Definition, Types & Strategy

Best for Pricing: Power BI

While Cognos Analytics offers some lower-level tool features at a low price point, Power BI offers more comprehensive and affordable entry-level packages to its users.

Microsoft is very good at keeping prices low as a tactic for growing market share. It offers a lot of features at a relatively low price. Power BI Pro, for example, costs approximately $10 per user per month, while the Premium plan is $20 per user per month. Free, somewhat limited versions of the platform are also available via Power BI Desktop and free Power BI accounts in Microsoft Fabric.

The bottom line for any rival is that it is hard to compete with Microsoft Power BI on price, especially because many of its most advanced features — including automated ML capabilities and AI-powered services — are available in affordable plan options.

IBM Cognos Analytics, on the other hand, has a reputation for being expensive. It is hard for IBM to compete with Power BI on price alone.

IBM Cognos Analytics pricing starts at $10 per user per month for on-demand cloud access and $5 per user per month for limited mobile user access to visuals and alerts on the cloud-hosted or client-hosted versions. For users who want more than viewer access and the most basic of capabilities, pricing can be anywhere from $40 to $450 per user per month.

Because of the major differences in what each product offers in its affordable plans, Microsoft wins on pricing.

Also see: Data Mining Techniques

Why Shouldn’t You Use Cognos or Power BI?

While both data and BI platforms offer extensive capabilities and useful features to users, it’s possible that these tools won’t meet your particular needs or align with industry-specific use cases in your field. If any of the following points are true for your business, you may want to consider an alternative to Cognos or Power BI:

Who Shouldn’t Use Cognos

The following types of users and companies should consider alternatives to Cognos Analytics:

• Users or companies with smaller budgets or who want a straightforward, single pricing package; Cognos tends to have up-charges and add-ons that are only available at an additional cost.
• Users who require extensive customization capabilities, particularly for data visualizations, dashboards, and data exploration.
• Users who want a more advanced cloud deployment option.
• Users who have limited experience with BI and data analytics technology; this tool has a higher learning curve than many of its competitors and limited templates for getting started.
• Users who are already well established with another vendor ecosystem, like Microsoft or Google.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who prefer to do their work online rather than on a mobile device; certain features are buggy outside of the mobile interface.
• Users who are not already well acquainted and integrated with the Microsoft ecosystem may face a steep learning curve.
• Users who prefer to manage their data in data warehouses rather than spreadsheets; while data warehouse and data lake integrations are available, including for Microsoft’s OneLake, many users run into issues with data quality in Excel.
• Users who prefer a more modern UI that updates in real time.
• Users who primarily use Macs and Apple products; some users have reported bugs when attempting to use Power BI Desktop on these devices.

Also see: Best Data Analytics Tools

If Cognos or Power BI Isn’t Ideal for You, Check Out These Alternatives

While Cognos and Power BI offer extensive features that will meet the needs of many BI teams and projects, they may not be the best fit for your particular use case. The following alternatives may prove a better fit:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

The two products in this comparison guide were assessed through a combination of reading product materials on vendor sites, watching demo videos and explanations, reviewing customer reviews across key metrics, and directly comparing each product’s core features through a comparison graph.

Below, you will see four key review categories that we focused on in our research. The percentages used for each of these categories represent the weight of the categorical score for each product.

User experience – 30%

Our review placed a heavy emphasis on user experience, considering both ease of use and implementation as well as the maturity and reliability of product features. We looked for features like AI assistance and low-code/no-code capabilities that lessened the learning curve, as well as learning materials, tutorials, and consistent customer support resources. Additionally, we paid attention to user reviews that commented on the product’s reliability and any issues with bugs, processing times, product crashes, or other performance issues.

Advanced analytics and scalability – 30%

To truly do business intelligence well, especially for modern data analytics requirements, BI tools need to offer advanced capabilities that scale well. For this review, we emphasized AI-driven insights, visuals that are configurable and updated in real time, shareable and collaborative reports and dashboards, and comprehensive features for data preparation, data modeling, and data explainability. As far as scalability goes, we not only looked at the quality of each of these tools but also assessed how well they perform and process data on larger-scale operations. We particularly highlighted any user reviews that mentioned performance lag times or other issues when processing large amounts of data.

Integrations and platform flexibility – 20%

Because these platforms need to be well integrated into a business’s data sources and most-used business applications to be useful, our assessment also paid attention to how integrable and flexible each platform was for different use cases. We considered not only how each tool integrates with other tools from the same vendor but also which data sources, collaboration and communication applications, and other third-party tools are easy to integrate with native integrations and connectors. We also considered the quality of each tool’s APIs and other custom opportunities for integration, configuration, and extensibility.

Affordability – 20%

While affordability is not the be-all-end-all when it comes to BI tools, it’s important to many users that they find a tool that balances an accessible price point with a robust feature set. That’s why we also looked at each tool’s affordability, focusing on entry price points, what key features are and are not included in lower-tier pricing packages, and the jumps in pricing that occur as you switch from tier to tier. We also considered the cost of any additional add-ons that users might need, as well as the potential cost of partnering with a third-party expert to implement the software successfully.

Bottom Line: Cognos vs. Power BI

Microsoft is committed to investing heavily in Power BI and enhancing its integrations across other Microsoft platforms and a growing number of third-party solutions. Any organization that is a heavy user of Office 365, Teams, Dynamics, and/or Azure will find it hard to resist the advantages of deploying Power BI.

And those advantages are only going to increase. On the AI front, for example, the company boasts around 100,000 customers using Power BI’s AI services. It is also putting effort into expanding its AI capabilities, with the generative AI-driven Copilot now in preview for Power BI users. For users with an eye on their budget who don’t want to compromise on advanced analytics and BI features, Power BI is an excellent option.

But IBM isn’t called Big Blue for nothing. It boasts a massive sales and services team and global reach into large enterprise markets. It has also vastly expanded its platform’s AI capabilities, making it a strong tool for democratized data analytics and advanced analytics tasks across the board.

Where Cognos Analytics has its most distinct advantage is at the high end of the market. Microsoft offers most of the features that small, midsize, and larger enterprises need for analytics. However, at the very high end of the analytics market, and in corporate environments with hefty governance and reporting requirements or legacy and on-premises tooling, Cognos has carved out a strategic niche that it serves well.

Ultimately, either tool could work for your organization, depending on your budget, requirements, and previous BI tooling experience. The most important step you can take is to speak directly with representatives from each of these vendors, demo these tools, and determine which product includes the most advantageous capabilities for your team.

Read next: 10 Best Machine Learning Platforms

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As two well-regarded data analytics platforms in the BI space, users may have a difficult time deciding between Looker and Power BI for their data management requirements. There are arguments for and against each, and in this comparison guide, we’ll dive deeper into core features, pros, cons, and pricing for Looker and Power BI.

But before we go any further, here’s a quick summary of how each product stands out against its competitors:

• Looker: Best for current Google product users and others who are most interested in highly configurable and advanced analytics capabilities, including data visualizations and reporting. Looker Studio in particular balances ease of use with high levels of customization and creativity, while also offering users a lower-cost version of an otherwise expensive platform.
• Power BI: Best for current Microsoft product users and others who want an easy-to-use and affordable BI tool that works across a variety of data types and use cases. This is considered one of the most popular BI tools on the market and meets the needs of a variety of teams, budgets, and experience levels, though certain customizations and big data processing capabilities are limited.

Looker vs. Power BI at a Glance

What Is Looker?

Looker is an advanced business intelligence and data management platform that can be used to analyze and build data-driven applications, embed data analytics in key organizational tools, and democratize data analysis in a way that preserves self-service capabilities and configurability. The platform has been managed by Google since its acquisition in 2019, and because of its deep integration within the Google ecosystem, it is a favorite among Google Cloud and Workspace users for unified analytics projects. However, the tool also works well with other cloud environments and third-party applications, as it maintains a fairly intuitive and robust collection of integrations.

Key features of Looker

• Comprehensive data visualization library: In addition to giving users the ability to custom-configure their visualizations to virtually any parameters and scenarios, Looker’s data visualization library includes a wide range of prebuilt visual options. Traditional visuals like bar graphs and pie charts are easy to access, and more complex visuals — like heatmaps, funnels, and timelines — can also be easily accessed.
• “Blocks” code snippets: Instead of reinventing the wheel for certain code snippets and built-out data models, Looker Blocks offers prebuilt data models and code to help users quickly develop high-quality data models. Industry-specific, cloud-specific, and data-source-specific blocks are all available, which makes this a great solution for users of all backgrounds who want to get started with complex models more quickly.
• Governed and integrated data modeling: With its proprietary modeling language and emphasis on Git-driven data storage and rule development, users can easily build trusted and governed data sources that make for higher-quality and more accurate data models, regardless of how many teams are working off of these models.

Pros

• Looker comes with a large library of prebuilt integrations — including for many popular data tools — and also offers user-friendly APIs for any additional integrations your organization may need to set up.
• Looker’s visualizations and reports are easy to customize to your organization’s more specific project requirements and use cases; it also offers one of the more diverse visualization libraries in this market.
• LookML allows users to create centralized governance rules and handle version control tasks, ensuring more accurate outcomes and higher quality data, even as data quantities scale.

Cons

• On-premises Looker applications do not easily connect to Looker Studio and other cloud-based tools in user portfolios, which severely limits the ability to maintain data projects accurately and in real time for on-prem users.
• Looker uses its own modeling language, which can make it difficult for new users to get up and running quickly.
• Some users have had trouble with self-service research and the vendor’s documentation.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization solution that is one of the most popular data analytics tools on the market today. As part of the Microsoft Power Platform, the tool is frequently partnered with Microsoft products like Power Automate, Power Apps, and Power Pages to get the most out of data in different formats and from different sources. Its focus on ease of use makes it a leading option for teams of all backgrounds; especially with the growth of its AI-powered assistive features, visualization templates, and smooth integrations with other Microsoft products, it has become one of the best solutions for democratized data science and analytics.

Key features of Power BI

• AI-driven analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• Dynamics 365 integration: Power BI integrates relatively well with the Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Comprehensive mobile version: Unlike many other competitors in this space, Microsoft Power BI comes with a full-featured, designed-for-mobile mobile application that is available at all price points and user experience levels. With native mobile apps available for Windows, iOS, and Android, any smartphone user can quickly review Power BI visualizations and dashboards from their personal devices.

Pros

• Power BI can be used in the cloud, on-premises, and even as an embedded solution in other applications.
• The user interface will be very familiar to users who are experienced with Microsoft products; for others, the platform is accompanied by helpful training resources and ample customer support.
• This platform makes democratized data analytics simpler, particularly with AI-powered features and a growing generative AI feature set.

Cons

• While some users appreciate that Power BI resembles other Microsoft 365 office suite interfaces, other users have commented on the outdated interface and how it could be improved to look more like other cloud-based competitors.
• Especially with larger quantities of data, the platform occasionally struggles to process data quickly and accurately; slower load times, crashes, and bugs are occasionally introduced during this process.
• Visualizations are not very customizable, especially compared to similar competitors.

Best for Core Features: It Depends

Both Looker and Power BI offer all of the core features you would expect from a data platform, including data visualizations, reporting and dashboarding tools, collaboration capabilities, and integrations. They also offer additional features to assist users with their analytical needs. Power BI offers support through AI assistance and Looker supports users with prebuilt code snippets and a diverse integration and plugin marketplace.

Microsoft maintains a strong user base with its full suite of data management features and easy-to-setup integrations with other Microsoft tools. It can be deployed on the cloud, on-premises, and in an embedded format, and users can also access the tool via a comprehensive mobile application.

Looker is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights. Enterprises can use it for a wide variety of complex data mining techniques. It takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker is also tightly integrated with a great number of Google datasets and tools, including Google Analytics, as well as with several third-party data and business tools.

Looker earns good marks for reporting granularity, scheduling, and extensive integration options that create an open and governable ecosystem. Power BI tends to perform better than Looker in terms of breadth of service due to its ecosystem of Microsoft Power Platform tools; users also tend to prefer Power BI for a comprehensive suite of data tools that aren’t too difficult to learn how to use.

Because each tool represents such a different set of strengths, it’s a tie for this category.

Best for Ease of Use and Implementation: Power BI

In general, users who have tried out both tools find that Power BI is easier to use and set up than Looker.

Power BI provides users with a low-code/no-code interface as well as a drag-and-drop approach to its dashboards and reports. Additionally, its built-in AI assistance — which continues to expand with the rise of Copilot in Power BI — helps users initiate complex data analytics tasks regardless of their experience with this type of technology or analysis.

For some users, Looker has a steep learning curve because they must learn and use the LookML proprietary programming language to set up and manage their models in the system. This can be difficult for users with little experience with modeling languages, but many users note that the language is easy to use once they’ve learned its basics. They add that it streamlines the distribution of insights to staff across many business units, which makes it a particularly advantageous approach to data modeling if you’re willing to overcome the initial learning curve.

The conclusion: Power BI wins on general use cases for a non-technical audience whereas Looker wins with technical users who know its language.

Best for Advanced Data Analytics: Looker

While both tools offer unique differentiators for data analytics operations, Looker outperforms Power BI with more advanced, enterprise-level data governance, modeling, and analytics solutions that are well integrated with common data sources and tools.

Both tools offer extensive visualization options, but Looker’s data visualizations and reporting are more customizable and easier to configure to your organization’s specs and stakeholders’ expectations. Looker also streamlines integrations with third-party data tools like Slack, Segment, Redshift, Tableau, ThoughtSpot, and Snowflake, while also working well with Google data sources like Google Analytics. As far as its more advanced data analytics capabilities go, Looker surpasses Power BI and many other competitors with features like granular version control capabilities for reports, comprehensive sentiment analysis and text mining, and open and governed data modeling strategies.

However, Looker has limited support for certain types of analytics tasks, like cluster analysis, whereas Power BI is considered a top tool in this area. And, so far, Power BI does AI-supported analytics better, though Google does not appear to be too far behind on this front.

It’s a pretty close call, but because of its range of data analytics operations and the number of ways in which Google makes data analytics tasks customizable for its users, Looker wins in this category.

Also see: Best Data Analytics Tools 

Best for Integrations: It Depends

When it comes to integrations, either Power BI or Looker could claim the upper hand here.

It all depends on if you’re operating in a Microsoft shop or a Google shop. Current Microsoft users will likely prefer Power BI because of how well it integrates with Azure, Dynamics 365, Microsoft 365, and other Microsoft products. Similarly, users of Google Cloud Platform, Google Workspace, and other Google products are more likely to enjoy the integrated experience that Looker provides with these tools.

If your organization is not currently working with apps from either of these vendor ecosystems, it may be difficult to set up certain third-party integrations with Power BI or Looker. For example, connecting Power BI to a collaboration and communication tool like Slack generally requires users to use Microsoft Power Automate or an additional third-party integration tool. Looker’s native third-party integrations are also somewhat limited, though the platform does offer easy-to-setup integrations and actions for tools like Slack and Segment.

Because the quality of each tool’s integrations depends heavily on the other tools you’re already using, Power BI and Looker tie in this category.

Best for Pricing: Power BI

Power BI is consistently one of the most affordable BI solutions on the market. And while Looker Studio in particular helps to lower Looker’s costs, the platform is generally considered more expensive.

Power BI can be accessed through two main free versions: Power BI Desktop and a free account in Microsoft Fabric. The mobile app is also free and easy to access. But even for teams that require more functionality for their users, paid plans are not all that expensive. Power BI Pro costs only $10 per user per month, while Power BI Premium is $20 per user per month.

Looker, on the other hand, is more expensive, requiring users to pay a higher price for its enterprise-class features. The Standard edition’s pay-as-you-go plan costs $5,000 per month, while all other plans require an annual commitment and a conversation with sales to determine how much higher the costs will be.

Additionally, there are user licensing fees that start at $30 per month for a Viewer User; users are only able to make considerable changes in the platform as either a Standard User or a Developer User, which costs $60 and $125 per user per month respectively.

Power BI takes the lead when it comes to pricing and general affordability across its pricing packages.

Also see: Top Digital Transformation Companies

Why Shouldn’t You Use Looker or Power BI?

While Looker and Power BI are both favorites among data teams and citizen data scientists alike, each platform has unique strengths — and weaknesses — that may matter to your team. If any of the following qualities align with your organizational makeup, you may want to consider investing in a different data platform.

Who Shouldn’t Use Looker

The following types of users and companies should consider alternatives to Looker:

• Users who want an on-premises BI tool; most Looker features, including useful connections to Looker Studio, are only available to cloud users.
• Users who are not already working with other Google tools and applications may struggle to integrate Looker with their most-used applications.
• Users with limited computer-language-learning experience may struggle, as most operations are handled in Looker Modeling Language (LookML).
• Users who want a lower-cost BI tool that still offers extensive capabilities to multiple users.
• Users in small business settings may not receive all of the vendor support and affordable features they need to run this tool successfully; it is primarily designed for midsize and larger enterprises.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who need more unique and configurable visualizations to represent their organization’s unique data scenarios.
• Users who are not already working with other Microsoft tools and applications may struggle to integrate Power BI into their existing tool stack.
• Users who consistently process and work with massive quantities of data; some user reviews indicate that the system gets buggy and slow with higher data amounts.
• Users who work with a large number of third-party data and business apps; Power BI works best with other Microsoft tools, especially those in the Power Platform.
• Users who consistently need to run more complex analytics, such as predictive analytics, may need to supplement Power BI with other tools to get the results they need.

If Looker or Power BI Isn’t Ideal for You, Check Out These Alternatives

Both Looker and Power BI offer extensive data platform features and capabilities, as well as smooth integrations with many users’ most important data sources and business applications. However, these tools may not be ideally suited to your team’s particular budget, skill sets, or requirements. If that’s the case, consider investing in one of these alternative data platform solutions:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

Looker and Power BI were reviewed based on a few core standards and categories for which data platforms are expected to perform. The four categories covered below have been weighted according to how important they are to user retention over time.

User experience – 30%

When it comes to user experience, we paid attention to how easy each tool is to use and implement and how many built-in support resources are available for users who have trouble getting started. Additionally, we considered how well the platform performs under certain pressures, like larger data loads, security and user control requirements, and more complex modeling and visualization scenarios. Finally, we considered the availability of the tool in different formats and how well the tool integrates with core business and data applications.

Scalability and advanced analytics compatibility – 30%

Our review also considered how well each platform scales to meet the needs of more sophisticated analytics operations and larger data processing projects. We paid close attention to how the platform performs as data loads grow in size and complexity, looking at whether user reviews mention any issues with lag times, bugs, or system crashes. We also considered what tools were available to assist with more complex analytics tasks, including AI-powered insights and support, advanced integrations and plugins, and customizable dashboards and reports.

Integrability – 20%

We considered how well each tool integrated with other software and cloud solutions from the same vendor as well as how easy it is to set up third-party integrations either via prebuilt connectors or capable APIs. In particular, we examined how well each platform integrated with common data sources outside of its vendor ecosystem, including platforms like Redshift, Snowflake, Salesforce, and Dropbox.

Cost and accessibility – 20%

For cost and accessibility, we not only focused on low-cost solutions but also on how well each solution’s entry-level solutions perform and meet user needs. We assessed the user features available at each pricing tier, how quickly pricing rises — especially for individual user licenses or any required add-ons, and whether or not a comprehensive free version was available to help users get started.

Bottom Line: Looker vs. Power BI

Microsoft’s Power BI has consistently been among the top two and three business intelligence tools on the market, recruiting and retaining new users with its balance of easy-to-use features, low costs, useful dashboards and visualizations, range of data preparation and management tools, AI assistance, and Microsoft-specific integrations. It is both a great starter and advanced data platform solution, as it offers the features necessary for citizen data scientists and more experienced data analysts to get the most out of their datasets.

Power BI tends to be the preferred tool of the two because of its general accessibility and approachability as a tool, but there are certain enterprise user needs for reporting and analytics distribution where Looker far outperforms Power BI. And for those heavily leaning on Google platforms or third-party applications, Looker offers distinct advantages to skilled analysts.

Ultimately, Looker doesn’t really try to compete head-to-head with Microsoft, because they each target different data niches and scenarios. It’s often the case that prospective buyers will quickly be able to identify which of these tools is the best fit for their needs, but if you’re still not sure, consider reaching out to both vendors to schedule a hands-on demo.

Read next: Best Data Mining Tools and Software

The post Looker vs. Power BI: 2024 Software Comparison appeared first on eWEEK.

Topics we covered: 

• Let’s talk about the automating of data pipelines. What exactly does it mean for companies, and what are the challenges here?
• How do you recommend companies address these challenges with data pipelines and artificial intelligence?
• How is Ascend addressing the data pipeline needs of its clients?
• The future of data pipeline automation? What do you predict for the sector in the next 1-3 years?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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Open source intelligence is sourced from all corners of the web, and while that makes the data incredibly comprehensive, it also brings forth a large body of data that needs to be fact-checked and reviewed closely for the best possible results.

Let’s take a closer look at what open-source intelligence is, how it works, and how you can apply this type of intelligence to your business operations most effectively.

What Is Open Source Intelligence?

Open source intelligence is a type of data-driven intelligence that scours the internet and other public sources for information that’s relevant to a user’s query or search. Most often, OSINT is used to strategically collect information about a particular individual, group of people, organization, or other public entity.

Historically, OSINT developed before the internet and was a military espionage technique for finding relevant information about military enemies in newspapers, radio broadcasts, and other public data sources. While most data sources used for OSINT today are online or somehow digitized, OSINT analysts still have the option to collect physical data from public, open sources.

Also see: Top Data Visualization Tools

Passive vs. Active OSINT

Passive and active OSINT are both viable open source intelligence collection methods with different amounts of hands-on activity and in-depth research required.

With passive OSINT, users most often complete a simple search engine, social media, or file search or look at a website’s or news site’s homepage through a broad lens. They aren’t actively trying to collect highly specific information but rather are unobtrusively looking at the easiest-to-find, top-of-the-stack intelligence available. With this intelligence collection method, the goal is often to collect useful information without alerting targets or data sources to your intelligence collection activities.

When practicing active OSINT, the methods tend to be more intrusive and involved. Users may complete more complex queries to collect obscure intelligence and metadata from databases and network infrastructure, for example. They also might fill out a form or pay to get through a paywall for more information.

In some cases, active OSINT may even involve reaching out directly to sources for more information that is not publicly available or visible. While active OSINT is more likely to give users real-time, in-depth information than passive OSINT, it is much more difficult to do covertly and may lead you to legal troubles if your data collection methods aren’t careful.

Open Source Intelligence Data Sources

Open source intelligence can be sourced from any public dataset or property. These are some of the most common OSINT data sources from across the web:

• Social media platforms
• Public-facing websites
• News media
• Academic and scientific studies
• Internet of Things databases
• Business directories
• Financial reports
• Images and image libraries
• Public records, both digital and physical

Also see: Best Data Analytics Tools 

How Does Open Source Intelligence Work?

For individuals and organizations that want to take advantage of open source intelligence, a simple way to get started is with a search engine query. Often, asking the right question about the demographic information you need is the first step to finding relevant open source data entries that can lead to more detailed information.

Beyond using search engines for internet-wide data searches, you can also refine and focus your search on specific data platforms or databases, such as a certain social media platform. Depending on your goals and experience, you may also benefit from analyzing open source threat intelligence feeds and other sources that frequently update massive amounts of data.

If your data collection and analysis goals require you to work with big data sources like databases, data lakes, or live feeds, manual searches and research are ineffective. To quickly process and sort through large amounts of intelligence, you’ll want to consider investing in a web scraping or specialized OSINT tool that can automate and speed up the data analysis process.

OSINT Use Cases

Have you ever “Facebook stalked” someone you just met or Google searched your family’s last name to see what pops up? Both of these are simple examples of how even individuals practice a simplified form of open source intelligence in their daily lives.

Businesses, too, may collect OSINT without realizing it, but in most cases, they are collecting this kind of intelligence for a distinct competitive advantage or cause. Here are some of the most common OSINT use cases in practice today:

• Threat intelligence, vulnerability management, and penetration testing: Especially when used in combination with more comprehensive threat intelligence platforms, open source intelligence and data collection can give security analysts and professionals a more comprehensive picture of their threat landscape, any notable threat actors, and historical context for past vulnerabilities and attacks.
• Market research and brand monitoring: If you want to get a better look at both quantitative purchase histories and overall brand sentiment from customers, OSINT is an effective way to collect broad demographic intelligence about how your brand is performing in the eyes of the consumer. For this particular use case, you may conduct either passive or active OSINT in social media platforms, user forums, CRMs, chat logs, or other datasets with customer information.
• Competitive analysis: In a different version of the example above, you can complete OSINT searches on competitor(s) to learn more about how they’re performing in the eyes of customers.
• Geolocation data sourcing and analysis: Publicly available location data, especially related to video and image files, can be used to find an individual and/or to verify the accuracy of an image or video.
• Real-time demographic analyses over large populations: When large groups of people are participating in or enduring a major event, like an election cycle or a natural disaster, OSINT can be used to review dozens of social media posts, forum posts, and other consumer-driven data sources to get a more comprehensive idea of how people feel and where support efforts — like counterterrorism or disaster relief response, for example — may be needed.
• Background checks and law enforcement: While most law enforcement officials rely on closed-source, higher intelligence feeds for background checks and identification checks, OSINT sources can help fill in the blanks, especially for civilians who want or need to learn more about a person. Keep in mind that there are legal limits to how open source intelligence can be used to discriminate in hiring practices.
• Fact-checking: Journalists, researchers, and everyday consumers frequently use OSINT to quickly check multiple sources for verifiable information about contentious or new events. For journalistic integrity and ethical practice, it’s important to collect information directly from your sources whenever possible, though OSINT sources can be a great supplement in many cases.

Also read: Generative AI: 15 Enterprise Use Cases You Can Implement

10 OSINT Tools and Examples

Particularly for passive OSINT and simple queries, a web scraping tool or specialized “dork” query may be all that you need. But if you’re looking to collect intelligence on a grander scale or from more complex sources, consider getting started with one or several of the following OSINT tools:

1. Spyse: An internet asset registry that is particularly useful for cybersecurity professionals who need to find data about various threat vectors and vulnerabilities. It is most commonly used to support pentesting.
2. TinEye: A reverse image search engine that uses advanced image identification technology to deliver intelligence results.
3. SpiderFoot: An automated querying tool and OSINT framework that can quickly collect intelligence from dozens of public sources simultaneously.
4. Maltego: A Java-based cyber investigation platform that includes graphical link analysis, data mining, data merging, and data mapping capabilities.
5. BuiltWith: A tool for examining websites and public e-commerce listings.
6. theHarvester: A command-line Kali Linux tool for collecting demographic information, subdomain names, virtual host information, and more.
7. FOCA: Open source software for examining websites for corrupted documents and metadata.
8. Recon-ng: A command-line reconnaissance tool that’s written in Python.
9. OSINT Framework: Less of a tool and more of a collection of different free OSINT tools and resources. It’s focused on cybersecurity, but other types of information are also available.
10. Various data analysis and AI tools: A range of open source and closed source data analysis and AI tools can be used to scale, automate, and speed up the process of collecting and deriving meaningful insights from OSINT. Generative AI tools in particular have proven their efficacy for sentiment analysis and more complex intelligence collection methods.

More on a similar topic: Top 9 Generative AI Applications and Tools

Pros and Cons of Open Source Intelligence

Pros of OSINT

• Optimized cyber defenses: Improved risk mitigation and greater visibility into common attack vectors; hackers sometimes use OSINT for their own intelligence, so using OSINT for cyber defense is often an effective response.
• Affordable and accessible tools: OSINT data collection methods and tools are highly accessible and often free.
• Democratized data collection: You don’t need to be a tech expert to find and benefit from this type of publicly available, open source data; it is a democratized collection of valuable data sources.
• Quick and scalable data collection methods: A range of passive and active data sourcing methods can be used to obtain relevant results quickly and at scale.
• Compatibility with threat intelligence tools and cybersecurity programs: OSINT alone isn’t likely to give cybersecurity professionals all of the data they need to respond to security threats, but it is valuable data that can be fed into and easily combined with existing data sources and cybersecurity platforms.

Cons of OSINT

• Accessible to bad actors and hackers: Just like your organization can easily find and use OSINT, bad actors can use this data to find vulnerabilities and possible attack vectors. They can also use OSINT-based knowledge to disrupt and alter intelligence for enterprise OSINT activity.
• Limitations and inaccuracies: Public information sources rarely have extensive fact-checking or approval processes embedded into the intelligence collection process. Especially if multiple data sources share conflicting, inaccurate, or outdated information, researchers may accidentally apply misinformation to the work they’re doing.
• User error and phishing: Users may unknowingly expose their data to public sources, especially if they fall victim to a phishing attack. This means anyone from your customers to your employees could unintentionally expose sensitive information to unauthorized users, essentially turning that private information into public information.
• Massive amounts of data to process and review: Massive databases, websites, and social media platforms may have millions of data points that you need to review, and in many cases, those numbers are constantly growing and changing. It can be difficult to keep up with this quantity of data and sift through it to find the most important bits of intelligence.
• Ethical and privacy concerns: OSINT is frequently connected without the target’s knowledge, which is an issue with AI and ethics. Depending on the data source and sourcing method, this information can be used to harm or manipulate people, especially when it’s PII or PHI that has accidentally been exposed to public view.

Bottom Line: Using OSINT for Enterprise Threat Intelligence

Getting started with open source intelligence can be as simple as conducting a Google search about the parties in question. It can also be as complex as sorting through a publicly available big data store with hundreds of thousands of data entries on different topics.

Regardless of whether you decide to take a passive or active approach, make sure all members of your team are aware of the goals you have in mind with open source intelligence work and, more importantly, how they can collect that intelligence in a standardized and ethical manner.

Read next: 50 Generative AI Startups to Watch in 2023

The post Open Source Intelligence (OSINT) Guide appeared first on eWEEK.

Among the topics we discussed:

• As small and medium-sized businesses grapple with data management challenges, what issues do you see?
• How do you recommend addressing these challenges? What role can the cloud play?
• How is AWS addressing the SMB market in particular?
• The future of data management and the cloud? How can businesses prepare now for future changes?

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A data lake is a centralized repository that allows storage of large amounts of data in its native, raw format. On the other hand, a data warehouse is a repository that stores structured and semi-structured data from multiple sources for analysis and reporting purposes.

A data lakehouse aims to bridge the gap between these two data management approaches by merging the flexibility, scale and low cost of data lake with the performance and ACID (Atomicity, Consistency, Isolation, Durability) transactions of data warehouses. This enables business intelligence and analytics on all data in a single platform.

Jump to:

• What does a data lakehouse do?
• Difference between data lakehouse, data warehouse, and data lake
• 5 layers of data lakehouse architecture
• Key features of a data lakehouse
• Advantages of a data lakehouse
• Challenges of a data lakehouse
• Bottom line: the data lakehouse

What Does a Data Lakehouse Do? 

A data lakehouse leverages a data repository’s scalability, flexibility and cost-effectiveness, allowing organizations to ingest vast amounts of data without imposing strict schema or format requirements.

In contrast with data lakehouses, data lakes alone lack the governance, organization, and performance capabilities needed for analytics and reporting.

Data lakehouses also are distinct from data warehouses. Data warehouses use extract, load and transform (ELT), or alternatively use extract, transform, and load (ETL) processes to load structured data into a relational database infrastructure – a data warehouse supports enterprise data analytics and business intelligence applications. However, a data warehouse is limited by its inefficiency in handling unstructured and semi-structured data. Additionally, they can get costly as data sources and quantity grow over time.

Data lakehouses address the limitations and challenges of both data warehouses and data lakes by integrating the flexibility and cost-effectiveness of data lakes with data warehouses’ governance, organization, and performance capabilities.

The following users can leverage a data lakehouse:

• Data scientists can use a data lakehouse for machine learning, BI, SQL analytics and data science.
• Business analysts can leverage it to explore and analyze diverse data sources and business uses.
• Product managers, marketing professionals, and executives can use data lakehouses to monitor key performance indicators and trends.

Also see: What is Data Analytics

Deeper Dive: Data Lakehouse vs. Data Warehouse and Data Lake

We have established that data lakehouse is a product of data warehouse and data lake capabilities. It enables efficient and highly flexible data ingestion. Let’s take a deeper look at how they compare.

Data warehouse

The data warehouse is the “house” in a data lakehouse. A data warehouse is a type of data management system specially designed for data analytics; it facilitates and supports business intelligence (BI) activities. A typical data warehouse includes several elements, such as:

• A relational database.
• An ELT solution for preparing the data for analysis, statistical analysis, reporting, and data mining capabilities.
• A client analysis tools for data visualization.

Data lake

A data lake is the “lake” in a data lakehouse. A data lake is a flexible, centralized storage repository that allows you to store all your structured, semi-structured and unstructured data at any scale. A data lake uses a schema-on-read methodology, meaning there is no predefined schema into which data must be fitted before storage.

This chart compares data lakehouse vs. data warehouse vs. data lake concepts.

5 Layers of Data Lakehouse Architecture

The IT architecture of the data lakehouse consists of five layers, as follows:

Ingestion layer

Data ingestion is the first layer in the data lakehouse architecture. This layer collects data from various sources and delivers it to the storage layer or data processing system. The ingestion layer can use different protocols to connect internal and external sources, such as:

• Database management systems
• Software as a Service (SaaS) applications
• NoSQL databases
• Social media
• CRM applications
• IoT sensors
• File systems

The ingestion layer can perform data extraction in a single, large batch or small bits, depending on the source and size of the data.

​​Storage layer

The data lakehouse storage layer accepts all data types as objects in affordable object stores like AWS S3.

This layer stores structured, unstructured, and semi-structured data in open source file formats like Parquet or Optimized Row Columnar (ORC). A data lakehouse can be implemented on-premise using a distributed file system like Hadoop Distributed File System (HDFS) or cloud-based storage services like Amazon S3.

Also see: Top Data Analytics Software and Tools 

Metadata layer

This layer is very important because it serves as the origin of the data lakehouse. Metadata is data that provides information about other data pieces – in this layer, it’s a unified catalog that includes metadata for data lake objects. The metadata layer also equips users with a range of management functionalities, such as:

• ACID (Atomicity, Consistency, Isolation, Durability) transactions ensure atomicity, consistency, isolation, and durability for data modifications.
• File caching capabilities optimize data access by keeping frequently accessed files readily available in memory.
• Indexing accelerates queries by enabling swift data retrieval.
• Data versioning enables users to save specific versions of the data.

The metadata layer empowers users to implement predefined schemas to enhance data governance and enable access control and auditing capabilities.

API layer

The API layer is a particularly important component of a data lakehouse. It allows data engineers, data scientists, and analysts to access and manipulate the data stored in the data lakehouse for analytics, reporting, and other use cases.

Consumption layer

The consumption layer is the final layer of data lakehouse architecture – it is used to host tools and applications such as Power BI and Tableau, enabling users to query, analyze, and process the data. The consumption layer allows users to access and consume the data stored in the data lakehouse for various business use cases.

Key Features of a Data Lakehouse

• ACID transaction support: Many data lakehouses use a technology like Delta Lake (developed by Databricks) or implement ACID transactions to provide data consistency and reliability in a distributed environment.
• Single data low-cost data storage: Data lakehouse is a cost-effective option for storing all data types, including structured, semi-structured and unstructured data.
• Unstructured and streaming data support: While a data warehouse is limited to structured data, a data lakehouse supports many data formats, including video, audio, text documents, PDF files, system logs and more. A data lakehouse also supports real-time ingestion of data – and streaming from devices.
• Open formats support: Data lakehouses can store data in standardized file formats like Apache Avro, Parquet and ORC (Optimized Row Columnar).

Advantages of a Data Lakehouse

A data lakehouse offers many benefits, making it a worthy alternative solution to a standalone data warehouse or data lake. Data lakehouses combine the quality service and performance of a data warehouse with the affordability and flexible storage infrastructure of a data lake. Data lakehouse helps data users solve the following issues.

• Unified data platform: It serves as a structured and unstructured data repository, eliminating data silos.
• Real-time and batch processing: Data lakehouses support real-time processing for fast and immediate insight and batch processing for large-scale analysis and reporting.
• Reduced cost: Maintaining a separate data warehouse and data lake can be too pricey. With a data lakehouse, data management teams only have to deploy and manage one data platform.
• Better data governance: Data lakehouses consolidate resources and data sources, allowing greater control over security, metrics, role-based access, and other crucial management elements.
• Reduced data duplication: When copies of the same data exist in disparate systems, it is more likely to be inconsistent and less trustworthy. Data lakehouses provide organizations with a single data source that can be shared across the business, preventing any inconsistencies and extra storage costs caused by data duplication.

Challenges of a Data Lakehouse

A data lakehouse isn’t a silver bullet to address all your data-related challenges. The data lakehouse concept is relatively new and its full potential and capabilities are still being explored and understood.

A data lakehouse is a complex system to build from the ground up. You’ll need to either opt for an out-of-box data lakehouse solution whose performance is highly variable, depending on the query type and the engine processing it, or invest time and resources to develop and maintain your custom solution.

Bottom Line: the Data Lakehouse

The data lakehouse is a new concept that represents a modern approach to data management. It’s not an outright replacement for the traditional data warehouse or data lake but a combination of both.

Although data lakehouses offer many advantages that make it desirable, it is not foolproof. You must take proactive steps to avoid and manage the security risks, complexity, as well as data quality and governance issues that may arise while using a data lakehouse system. 

Also see: Generative AI and Data Analytics Best Practices 

The post What is a Data Lakehouse? Definition, Benefits & Features appeared first on eWEEK.