--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1366 reviews by the users' company size, role or industry to find out how Databricks works for a business like yours. aggregate_rating: rating_value: 4.6 review_count: 1366 scale: '5' date_modified: '2026-08-12' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Databricks Reviews **Vendor:** Databricks Inc. **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.6/5.0 **Total Reviews:** 1,366 ## About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. ## Databricks Pros & Cons **What users like:** - Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications. (192 reviews) - Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services. (154 reviews) - Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency. (141 reviews) - Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights. (114 reviews) - Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization. (112 reviews) - Scalability (111 reviews) - ML Integration (106 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (102 reviews) - Machine Learning (97 reviews) - Users value the **effective data management features** of Databricks, simplifying their workflows and enhancing decision-making. (87 reviews) **What users dislike:** - Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability. (78 reviews) - Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects. (71 reviews) - Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools. (64 reviews) - Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes. (45 reviews) - Users face **complex setup** challenges initially, though support helps simplify the experience over time. (35 reviews) - Performance Issues (34 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (34 reviews) - Poor UI Design (33 reviews) - Users express frustration over **missing features** in Databricks, limiting its effectiveness for complex deployments and custom setups. (31 reviews) - Cost (29 reviews) ## Databricks Reviews ### 1. I love databricks **Rating:** 5.0/5.0 stars **Reviewed by:** taka b. | engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 11, 2025 **What do you like best about Databricks?** What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments. **What do you dislike about Databricks?** One downside is that the initial setup and networking configuration can be complex and require technical expertise. Also, the cost can scale up quickly depending on usage, so cost monitoring is essential. Additionally, the lack of comprehensive documentation in some languages like Japanese can be a limitation. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve challenges related to managing and analyzing large volumes of data across multiple sources. It simplifies ETL pipelines, improves data reliability through Delta Lake, and enables scalable machine learning. This has significantly reduced the time our team spends on data preparation and model training, leading to faster business insights and better decision-making. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks has helped solve challenges related to managing and analyzing large volumes of data for your team, leading to faster business insights and better decision-making. We appreciate your support and are committed to continuously improving our platform. ### 2. Lakebase: Powering Data and AI together **Rating:** 5.0/5.0 stars **Reviewed by:** Ajay P. | Manager - Data, AI & Automation, Small-Business (50 or fewer emp.) **Reviewed Date:** September 08, 2024 **What do you like best about Databricks?** I use Lakebase within Databricks as the foundation for our AI solutions, where data models and applications work together seamlessly. Lakebase provides a single unified data foundation to build AI directly on consistent, real-time data. I also like Agent Bricks in Databricks because it helps us quickly build intelligent AI agents and automate workflows using that data. The ease of setup was a significant plus for us, as it was super easy to get started. **What do you dislike about Databricks?** Agentbricks needs more native integration to reduce the manual setup and speed up the workflow automation. **What problems is Databricks solving and how is that benefiting you?** Databricks - Lakebase helps us bring data and AI together on one platform, reducing complexity and avoiding data movements. Agent Bricks allows us to quickly build intelligent AI agents and automate workflows using real-time data. **Official Response from Jess Darnell:** > We're thrilled to hear that you find Lakebase simple to use and packed with features for developing data pipelines and AI. It's great to know that it has been helpful in implementing GenAI and integrating with different sources through LakeFlow. ### 3. Databricks Genie Code - Agentic Applied AI for end-end SDL liefecycle **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Senior Cloud Solution Architect - Accenture Data & AI (Applied Intelligence), Enterprise (> 1000 emp.) **Reviewed Date:** October 03, 2023 **What do you like best about Databricks?** Genie Code 1) Genie Code automated our ETL processes, reducing manual effort and increasing efficiency. With Agentic’s SDL, we implemented CI/CD pipelines for faster, seamless updates and deployments. 2) Genie Code streamlined complex STTM mappings, improving accuracy and speed. Agentic’s real-time updates ensured mapping adjustments were made dynamically to align with changing transaction data. 3) We defined automated unit tests using SKILL.md, ensuring data transformations are validated before deployment. This reduced errors and ensured data quality, boosting confidence in our analytics. 4) Using Skills.md, we added custom extensions to Genie Code, such as integrating third-party data for enriched reports. This agility allowed us to quickly adapt to business needs and deliver new capabilities. 5) Agentic’s SDL enabled real-time data processing, providing immediate analytics for decision-making. Our marketing and sales teams now act on fresh data instantly, improving response times and overall efficiency. **What do you dislike about Databricks?** Hope it can be improved in next update - Debugging issues in complex workflows can be time-consuming due to limited visibility into intermediate data transformations. Genie Code lacks advanced error recovery mechanisms, making it difficult to manage failures in large-scale data pipelines. As data volume increases, Genie Code’s performance can degrade, requiring significant manual adjustments to ensure smooth operation at scale. **What problems is Databricks solving and how is that benefiting you?** 1) Scalable Processing - Built on Databricks' Spark-based architecture, Genie Code efficiently handles and scales processing for massive datasets, ensuring performance even with increasing data volumes. 2) Genie Code automates end-to-end ETL workflows, from data extraction to transformation and loading, streamlining data operations and eliminating manual tasks. 3) Real time collaboration - Genie Code enables real-time collaboration across teams by using shared notebooks, making it easier for data professionals to build and refine workflows collectively. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks Data Intelligence Platform is helping you with unified lakehouse platform, workflow orchestration, integrations, and data sharing. We are committed to providing solutions that meet your business needs. ### 4. Driving AI and Data Innovation with a Unified Databricks Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Ajay Kumar P. | Associate Consultant-Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 09, 2023 **What do you like best about Databricks?** I use Databricks for ETL, Reporting, and AI, and I appreciate that it works as one unified solution for all data and AI needs. It makes it easier to track data and create insights, helping us deal with data silos. I like the Unity Catalog as it helps us manage and govern data in one place. I also like using AgentBricks as a multi-agent system for creating AI applications from PDFs and other documents. I find Genie valuable as it allows business users to ask questions in natural language and get exact answers. The initial setup of Databricks was very easy, making the transition smooth. **What do you dislike about Databricks?** I think workflow could be improved by adding multiple triggers to the same pipeline, as for now, if we want to schedule the same pipeline multiple times in a day, we have to clone it for each time. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to eliminate data silos and make data tracking and insights creation easy. Unity Catalog manages data governance, AgentBricks develops AI applications, and Genie provides answers using natural language on structured data. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks Intelligence Platform is providing value by addressing data governance issues and streamlining data management. Your feedback on the need for more robust workflows is noted, and we are committed to continuously improving our platform to better meet the needs of Data Engineers, ML Engineers, and Analysts. ### 5. Outstanding Experience with This Software **Rating:** 4.0/5.0 stars **Reviewed by:** Kriti K. | CFO, Mid-Market (51-1000 emp.) **Reviewed Date:** January 09, 2026 **What do you like best about Databricks?** Databricks data intelligence is a platform that helps in accommodating all of our business and official data and share it with different team departments so that they can analyse it and create a detailed analytics of past performances and also make required changes on it for future growth. **What do you dislike about Databricks?** One of the major challenge that we face while working with Databricks data intelligence platform is that you cannot use this tool with a single data scientist you will have to keep a team of professionals who can deal with large data and create multiple graphs and analytics according to available information and this complete activity involves lot of financial investment **What problems is Databricks solving and how is that benefiting you?** This software help us in making sure that all the data of different departments are accommodated in a same software so that access can be easier and decisions can be taken much quicker. With the help of this tool data of all the departments like finance, operations, sales and marketing are screend in one time and thoroughly interchecked too. **Official Response from Janelle Glover:** > It's fantastic to hear how Databricks Data Intelligence Platform is benefiting your business by streamlining access to data from different departments and facilitating quicker decision-making. We appreciate your feedback on the financial investment and will take it into consideration as we continue to improve user experience and ensure our platform is accessible for all users. ### 6. Finally Databricks Data Intelligence Platform has given us stability with Spark jobs. **Rating:** 5.0/5.0 stars **Reviewed by:** Preetham C. | Senior Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 06, 2026 **What do you like best about Databricks?** I am glad my leg is not completely dead from having to wait for a query (at least the auto termination feature works and saved me money) History is somewhat useful I rely on it to pull up previous scripts I write because I always seem to forget what I did the day before. I think it helps keep finance off my back by saving them money on the AWS bill. To me, it's simply one central location for managing our big data so I do not have to watch 5 monitors. **What do you dislike about Databricks?** If I want to find a query that was run over 30 days ago the search function does not even come close to being able to help me and also installing R Packages is a complete nightmare and the error messages are as clear as mud. And when working with large amounts of data the user interface becomes sluggish and trying to scroll through a notebook is like pushing through the mud. It would be nice if it were easier to install/ manage external spark packages. **What problems is Databricks solving and how is that benefiting you?** We create Machine Learning Models and perform Heavy ETL for the Analytics Department. It does a good job partitioning our data so we can query smaller time frames without having to sit around for hours waiting. It also allows our data team to focus on their actual work rather than constantly fixing broken clusters which is nice. **Official Response from Janelle Glover:** > Thank you for taking the time to share your feedback! We're glad to hear that our platform has provided stability for your Spark jobs and that the auto-termination feature has been beneficial in saving costs. We appreciate your notes on the history feature and the centralization of big data management and will take these concerns into consideration. ### 7. Databricks Data Intelligence Platform actually works and saves money **Rating:** 5.0/5.0 stars **Reviewed by:** Christopher C. | Sales Operations Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** December 21, 2025 **What do you like best about Databricks?** The autoscale works well; it also helped us reduce the cost of using cloud resources. I thought that was going to be a problem since this is the first time we used autoscale. The support has been good enough, they normally appear on time for their scheduled hours to assist us in fixing the problems we create. The ability to save the query history in order of how the queries were written is nice as I often forget what I write a few minutes after writing it. When working with coworkers who need access to your code you can send them the permalink (link) to the code which is better than having to explain it. Since it supports both Spark and Presto within one tool I do not have to jump between tools. **What do you dislike about Databricks?** I hate the way the search function works. I have never found the results from a month ago, and it is annoying. The UI will occasionally lag behind my typing, and if I am in a rush I feel it takes too long. Also it would make much more sense for the tables drawer to remain open when I click on the notebook and instead of closing automatically which is very time consuming as I need to reopen it again. I have had the support team tell me they cannot replicate an issue which is no help at all. **What problems is Databricks solving and how is that benefiting you?** We are able to process large amounts of data using Databricks without having to build out a large ops team to manage our clusters. We can scale up and down so we only pay for the time we are running jobs in the cloud. I use it to see sales numbers on a daily basis although I am not a data engineer. Databricks allows us to run pipelines with Airflow without all of the things crashing right away. Databricks reduces the amount of admin work involved in building and managing clusters. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks Data Intelligence Platform! We're pleased to hear that the platform has helped you save costs and streamline your data processing. We understand your concerns about the search function and UI, and we'll work on addressing these issues to enhance your user experience. We appreciate your feedback! ### 8. Unified, User-Friendly Platform That Accelerates Data Insights **Rating:** 4.5/5.0 stars **Reviewed by:** ANIKET S. | Student, Mid-Market (51-1000 emp.) **Reviewed Date:** December 19, 2025 **What do you like best about Databricks?** What I like best about the Databricks Data Intelligence Platform is how it brings everything data engineering, analytics, and machine learning together in one unified environment. It’s very user-friendly despite being powerful, and it makes collaboration between technical and non-technical teams much easier. The platform handles large volumes of data efficiently, scales smoothly, and integrates well with cloud services, which saves a lot of time and effort. Overall, it helps turn raw data into meaningful insights faster without making the process overly complex. **What do you dislike about Databricks?** One thing I dislike about the Databricks Data Intelligence Platform is that it can feel complex and overwhelming for new users, especially those without a strong technical background. The learning curve is quite steep, and it takes time to fully understand how to use all the features effectively. Additionally, the pricing can become expensive as usage scales, and cost management isn’t always very transparent. Sometimes, debugging errors or performance issues can also be challenging, particularly in large or highly integrated workflows. **What problems is Databricks solving and how is that benefiting you?** Databricks Data Intelligence Platform solves the problem of working with fragmented data systems by bringing data engineering, analytics, and machine learning into a single, unified platform. Instead of managing multiple tools, everything is available in one place, which reduces complexity and data silos. This benefits me by saving time, improving collaboration across teams, and enabling faster access to reliable insights. It also handles large-scale data processing efficiently, allowing better decision-making based on real-time and accurate data rather than delayed or incomplete information. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of the Databricks Data Intelligence Platform in solving the challenges of fragmented data systems. We are committed to providing a unified platform that saves time, improves collaboration, and enables faster access to reliable insights. We also appreciate your feedback about the learning curve and pricing concerns. We are constantly working to improve user experience and transparency, and your input is valuable in that process. ### 9. Powerful unified data platform with great collaboration **Rating:** 4.5/5.0 stars **Reviewed by:** Varun G. | Project Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** December 13, 2025 **What do you like best about Databricks?** Databricks is easy to use and simple to get started with, even when handling large amounts of data. It brings everything like data processing, analytics, and AI into one place, so my team do not need multiple tools. The platform integrates smoothly with common tools like BI dashboards, workflows, and cloud services. Its notebooks make day to day work frequent and convenient by allowing our teams to collaborate in real time. There are many built in features for data, analytics, and machine learning without added complexity. Customer support, documentation, and community resources make it easier to solve issues quickly. **What do you dislike about Databricks?** Databricks can be expensive and unpredictable in cost, especially for small teams if workloads run longer than expected. It takes technical expertise to set up, manage, and optimize performance, which can be challenging for non-technical users. Costs need frequent monitoring since compute and storage are billed separately. While it has basic dashboards, it still depends on tools like Power BI or Tableau for full reporting. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the problem of managing data across many different tools by putting everything in one place. This saves time, reduces confusion, and makes it easier for teams to work together. It helps us handle large data smoothly, keep access and security under control, and get useful insights faster even for people who are not very technical. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of using Databricks for managing data and improving collaboration within your team. We understand the importance of cost predictability and ease of use, and we're actively working to enhance these aspects of our platform. ### 10. Unified Data Platform That Simplifies Complex Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** PRATYUSH A. | Student and researcher, Small-Business (50 or fewer emp.) **Reviewed Date:** December 13, 2025 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning together on one platform. Having a unified environment built around Apache Spark makes collaboration between data teams much easier. The Lakehouse approach works well because it removes the need to move data across multiple tools. Performance is strong for large datasets, and notebooks make experimentation, analysis, and collaboration more efficient. Overall, it simplifies complex data workflows while still being powerful. **What do you dislike about Databricks?** The main downside is the cost and complexity for new users. Pricing can be hard to predict, especially when workloads scale unexpectedly, and compute costs can rise quickly if not monitored closely. There is also a learning curve for teams that are not already familiar with Spark or cloud-based data platforms. Some advanced configurations and optimizations require experienced resources, which can slow adoption for smaller or less mature data teams. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us solve the problem of working with large, fragmented datasets across different tools and teams. Earlier, data engineering, analytics, and machine learning were handled in separate systems, which created silos and slowed down insights. With Databricks, we can process, analyze, and model data on a single platform, which improves collaboration and reduces data movement. From a business perspective, it helps us generate insights faster, scale analytics as data grows, and improve data reliability. This leads to quicker decision-making, more consistent reporting, and better use of data for forecasting and optimization, while reducing operational overhead. **Official Response from Janelle Glover:** > It's great to hear that Databricks has helped streamline your data workflows and improve collaboration across teams. We recognize the challenges around cost, complexity, and the learning curve, and we are dedicated to addressing these concerns to ensure a more seamless experience for all users. Thank you for your valuable feedback. ### 11. Simplifies Data Analysis for Streamlined Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Pragati V. | Social Media Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** January 06, 2026 **What do you like best about Databricks?** Databricks is a Data Analytics tool that is used by our designing engineers to analyse all the different type of financial and operational data of our company and create a summary of everything so that understanding of companies workflow can be simple. **What do you dislike about Databricks?** One of the major disadvantage of databricks is that the software is completely based on programming language like Python, SQL, Scala so if the user does not have knowledge of these programming languages than it is very difficult for them to use this platform. **What problems is Databricks solving and how is that benefiting you?** The best part of databricks is that it can easily get integrated with cloud networks like as azure, AWS, Google Cloud etc. So, working on this platform also help us in getting access to all the data of different years and compile them to get a proper analytics of business performance. **Official Response from Janelle Glover:** > It's great to hear that Databricks has helped you integrate with cloud networks and access historical data for business analytics. We understand that the programming language requirement may be a challenge for some users, and we appreciate you sharing your concerns with this. ### 12. The smoothest my big data work has ever felt **Rating:** 4.0/5.0 stars **Reviewed by:** Donnie M. | Chief Revenue Officer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 16, 2025 **What do you like best about Databricks?** I mostly use the Databricks Data Intelligence Platform to mangle large datasets that we store across cloud buckets and create etl pipelines, as well as stand up notebooks on which I do a lot of explorative work. I very much like that everything feels ready to go such as clusters start quickly, scaling just works in the background and I can really stop worrying about infrastructure stuff and focus on analysis. **What do you dislike about Databricks?** The UI can feel slow especially when I’m deep in the middle of a heavy notebook session and sometimes things are just SLOW to click or jobs don’t cancel when I ask it to. And I’ve only just started messing around with the cluster software actually, and while it’s powerful there’s definitely a learning curve as I still dig through stuff on occasion trying to figure out which cluster or runtime setting is making things run differently than others. **What problems is Databricks solving and how is that benefiting you?** Databricks has, quite literally, taken the hassle out of dealing with big data infrastructure and instead of waiting for clusters to spin up and spending time hunting down why jobs failed, I can open a notebook and start working. That change alone has accelerated our etl development, lowered our cloud expenses significantly and made it far less risky to experiment with ml workflows without having to think about scaling. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks Data Intelligence Platform has made your big data work feel smooth and hassle-free. Our goal is to provide a platform that allows you to focus on analysis without worrying about infrastructure. We appreciate your feedback about the UI and cluster software, and we're continuously working to improve the user experience and reduce any performance issues. ### 13. Best data all in one solution **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Leisure, Travel & Tourism | Enterprise (> 1000 emp.) **Reviewed Date:** January 12, 2023 **What do you like best about Databricks?** Pyspark, Delta lake, The way that it integrates seamlessly with AWS services and how they managed to open source everything. It provides a great managed spark infrastructure. **What do you dislike about Databricks?** Harder to integrate with more legacy data sets. Requires you to move data into AWS to use. **What problems is Databricks solving and how is that benefiting you?** Databricks is creating a solution that allows us to query and manage our data lake with immense performance. Delta lake ensures ACID transactions on data and the query performance from databricks is unmatched **Official Response from Aunalisa Arellano:** > We're glad to hear that you enjoy the seamless integration with AWS services and the managed spark infrastructure. We understand the challenges of integrating with legacy data sets and having to move data into AWS, and we're continuously working on improving our platform's compatibility and flexibility. ### 14. All-in-One Data Platform with Seamless Integration and Top Performance **Rating:** 5.0/5.0 stars **Reviewed by:** Rahul D. | Program Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** December 18, 2025 **What do you like best about Databricks?** I like how Databricks brings data engineering, analytics, and AI into a single platform. The Spark and Delta Lake integration is seamless, performance is excellent, and collaboration through notebooks is very smooth. Its tight integration with Azure services also makes data pipelines, security, and scaling much easier. **What do you dislike about Databricks?** The main downside is the cost, especially when running large or long-running workloads. Debugging complex Spark jobs can also be challenging at times, and fine-tuning performance often requires a good understanding of Spark internals. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of processing and managing large-scale data efficiently by providing a unified platform for data engineering, analytics, and AI. It reduces pipeline complexity, improves performance, and enables faster development and collaboration, helping us deliver reliable data solutions with less operational overhead. **Official Response from Janelle Glover:** > It's great to hear that Databricks Data Intelligence Platform is helping you solve the challenge of processing and managing large-scale data efficiently. We acknowledge the concerns about cost and debugging complex Spark jobs, and we are actively working on improvements to address these issues. ### 15. Comprehensive Data Platform with Flexible Onboarding and Robust Governance **Rating:** 5.0/5.0 stars **Reviewed by:** Awadhesh P. | Solution Architect - Data & AI, Enterprise (> 1000 emp.) **Reviewed Date:** January 08, 2026 **What do you like best about Databricks?** This is an end-to-end platform that begins with flexible onboarding of data from multiple sources, followed by processing through a medallion architecture. The Unity Catalog is used for governance, cataloging, and tracking data lineage. Databricks SQL serves as the endpoint for use cases such as business intelligence, as well as downstream integration through API endpoints. **What do you dislike about Databricks?** There isn't anything particularly specific, but in certain situations, we do depend on cloud-native services. For instance, when working on Azure and aiming for comprehensive end-to-end governance, we require Azure Purview. **What problems is Databricks solving and how is that benefiting you?** As a solution architect specializing in Data & AI, Databricks has become my preferred platform for all things related to data engineering, data warehousing, and analytics. In my experience designing solutions, I have found that Databricks offers a comprehensive suite that meets the majority of client needs. This means there is no need to search across multiple vendors for the best services, as most requirements can be addressed within a single platform. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks Data Intelligence Platform. It's great to hear that it has become your preferred platform for data engineering, warehousing, and analytics. ### 16. Why I love Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Sravya M. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** January 06, 2026 **What do you like best about Databricks?** I like the integration between Spark, Delta Lake, and notebooks, which helps optimize and scale data pipelines. And the other feature that I love is the different cluster options that we have, which are really easy to tune for cost and performance. **What do you dislike about Databricks?** Debugging complex Spark jobs and understanding some of the optimization features is a time taking process in Databricks, is what I think. **What problems is Databricks solving and how is that benefiting you?** We basically get data into our catalogs in Databricks. The Databricks here allow us to perform many transformations and create different data products for the end user. Not just the data products, there are many other benefits like the ease of workspace, notebooks, and the cluster configuration. **Official Response from Janelle Glover:** > Thank you for sharing what you like best about the Databricks Data Intelligence Platform. We appreciate your feedback on the challenges you've faced with debugging and optimization. We are committed to addressing these issues and enhancing the platform to provide a better user experience. ### 17. Databricks -Scalable Data **Rating:** 4.0/5.0 stars **Reviewed by:** Vishal D. | Sales Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** January 13, 2026 **What do you like best about Databricks?** 1. Easy for data teams once set up; notebooks, SQL, and dashboards work smoothly in one place. 2. Used frequently for data engineering, analytics, and ML workloads. 3. Implementation is structured and scalable, especially on cloud environments. **What do you dislike about Databricks?** 1. Customer support quality depends on the support tier purchased. 2. Too many advanced features can feel overwhelming for smaller teams. 3. Initial setup and architecture planning take time and skilled resources. **What problems is Databricks solving and how is that benefiting you?** 1. Helps me make faster, data-driven decisions with scalable and trusted data pipelines. 2. Handles large data volumes reliably, supporting daily and recurring workloads. 3. Reduces time spent managing infrastructure so I can focus on insights and outcomes. **Official Response from Janelle Glover:** > It's great to hear that Databricks is helping you make faster, data-driven decisions and reducing time spent on infrastructure management. We appreciate your feedback on our customer support, the initial set up, and the learning curve of our advanced features and will take these items into consideration as we continuously work to enhance our user experience. ### 18. Feature-Rich, Reliable, but Browser Coding Needs Improvement **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** June 28, 2024 **What do you like best about Databricks?** I use Databricks for Data Engineering and Data Science, and it gives me access to data and powerful compute to answer analysis quickly and build pipelines for continuous tracking. I like that Databricks is always improving and it's a feature-packed enriched platform. It helps lower my time to insight, and I never worry about lagging or doubting that the tools I need to succeed aren't available. **What do you dislike about Databricks?** Coding in a web browser is difficult, slow, lags, and it's a memory hog. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to access data effortlessly and utilize powerful compute for quick analysis and pipeline building, which lowers my time to insight without doubting tool availability. **Official Response from Janelle Glover:** > We appreciate your feedback on the continuous development of our platform and the benefits of tools like Mosaic Composer. Your input on the configurability options is valuable and will be shared with our team for consideration. Thanks for taking the time to share your thoughts! ### 19. Unified Platform Enhances Data Collaboration and Processing **Rating:** 4.0/5.0 stars **Reviewed by:** Monazah S. | DevOps Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 12, 2026 **What do you like best about Databricks?** I like the Databricks Data Intelligence Platform because it's a unified, scalable platform for data engineering, analytics, and collaboration. It simplifies the process of building and running data pipelines, handles performance-intensive Spark workloads, and promotes collaboration across teams with shared notebooks and environments. It's great for managing and processing large datasets, improving performance, and providing a consistent platform for turning raw data into insights. The initial setup was easy, thanks to helpful documentation. **What do you dislike about Databricks?** It can improve in areas like startup time for clusters, deeper visibility into performance tuning and clearer documentation for advanced configurations. **What problems is Databricks solving and how is that benefiting you?** I use the Databricks Data Intelligence Platform for large-scale data processing and analytics. It simplifies building reliable data pipelines, handles Spark workloads efficiently, reduces operational overhead, and enables team collaboration with shared notebooks. It's great for turning raw data into actionable insights. **Official Response from Janelle Glover:** > We're glad to hear that you find our platform unified, scalable, and easy to use for managing and processing large datasets. We appreciate your feedback and will take your suggestions for improvement into consideration. ### 20. User-Friendly Platform with Outstanding Support **Rating:** 5.0/5.0 stars **Reviewed by:** Cynthia W. | Data Analyst, Biotechnology, Mid-Market (51-1000 emp.) **Reviewed Date:** December 17, 2025 **What do you like best about Databricks?** I have used approx. number of Data Analytics platform but the user friendly enviornment and features of Databricks gives always reliable and satisfactory experience to me that gives a confidence to handle large data sets without any problem. **What do you dislike about Databricks?** Never have any issue with their services instead they gives a best user support to us to manage all our AIML integrations and new AI implementations. **What problems is Databricks solving and how is that benefiting you?** We have mainly use Databricks for our data warehousing and analytical use cases and also their multiple features like AIML and ETL integration gives best infrastructure to manage all data related task at one place without any issue and confusion. **Official Response from Janelle Glover:** > It's great to hear that Databricks has been instrumental in solving your data warehousing and analytical use cases. We strive to provide a comprehensive platform with seamless AIML and ETL integration for our users' benefit. ### 21. A game changer for handling large data **Rating:** 5.0/5.0 stars **Reviewed by:** Naga Likhita C. | Technical Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** September 10, 2025 **What do you like best about Databricks?** Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze. **What do you dislike about Databricks?** The initial setup was a bit confusing, and some of the advanced features could use better documentation. Figuring out the pricing took some time, but once we got going, the benefits were clear. **What problems is Databricks solving and how is that benefiting you?** Databricks is helping me tackle the challenge of working with huge amounts of data for analytics and machine learning. Before using the platform, processing and distributing big datasets was slow and complicated. Now, I can run large-scale data science experiments and build models much faster. The platform’s tools make it easier to collaborate, share results, and turn data into insights that actually help my team make better decisions. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks is helping you tackle the challenge of working with massive datasets for analytics and machine learning. Our goal is to make data processing and collaboration easier, ultimately helping teams make better decisions based on insights. ### 22. A Game-Changer for Data Teams **Rating:** 4.0/5.0 stars **Reviewed by:** Varun T. | Senior Software Engineer, Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** September 08, 2025 **What do you like best about Databricks?** I am using data bricks from around 1 year. In a week I use it approx 3-4 days. Everything is integrated, which means I don’t have to switch between multiple tools to do different tasks. It really improves team collaboration. Sharing notebooks and collaborating on models is super easy. This has been great for our team since we often work together on projects and need to see each other's code and progress. **What do you dislike about Databricks?** This is probably the biggest downside. Databricks can get pretty expensive, especially if you’re not monitoring your usage carefully. It’s definitely a platform that’s best suited for teams with a larger budget or organizations that need heavy-duty data processing. **What problems is Databricks solving and how is that benefiting you?** Managing and processing huge volumes of data used to be a huge headache. As our data grew, our previous tools were struggling to keep up. Databricks has solved this by providing a robust, scalable infrastructure that automatically adjusts to handle large datasets. Whether it’s batch processing or streaming data, we no longer worry about performance issues when scaling up. **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks Data Intelligence Platform. We appreciate your feedback about the cost and are committed to providing value for your investment. ### 23. It's super handy for analytics, scales well, and you can easily rely on it. **Rating:** 4.0/5.0 stars **Reviewed by:** Omar J. | Full Stack Web Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 26, 2025 **What do you like best about Databricks?** The best thing about Databricks is that it very easily consolidates data engineering, data science, and analytics – all in one place Therefore, I can process huge data sets quickly, run very complex machine learning operations, all without switching tools. Collaboration through notebooks with my team in real-time really reduced a lot of back and forth that I used to have. **What do you dislike about Databricks?** The big issue is pricing can quickly ramp up if I’m not careful with cluster size or if I forget to turn them off. I had to learn how to use some of the more advanced features, like Unity Catalog and MLflow connectors if I was to use them well. The only thing I would add is an easy interface, like in some BI products. It may be overwhelming for the newbie. **What problems is Databricks solving and how is that benefiting you?** I can construct fraud detection models, run queries over billions of rows, and test proof-of-concepts for clients all on one platform. This cost me days in setting work up. Huge workloads on peak hours will not slow me down as I can scale my computing power instantly. This is why I think Databricks gives me more freedom to play around with data models and machine learning at scale as compared to Snowflake or any other alternative. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks Data Intelligence Platform handy for analytics and that it consolidates data engineering, data science, and analytics in one place. We understand your concern about pricing and the learning curve for advanced features. We appreciate your feedback and will continue to work on improving the user experience and pricing. ### 24. Great platform for big data and ML, with some learning curve **Rating:** 4.5/5.0 stars **Reviewed by:** Amit K. | Build Connx, Small-Business (50 or fewer emp.) **Reviewed Date:** August 08, 2025 **What do you like best about Databricks?** What I really like about Databricks is how it brings everything—data engineering, analytics, and machine learning—into one place. It saves a lot of time when switching between workflows. The collaborative notebooks are super handy when working with a team, and the Spark integration just works without much hassle. Delta Lake is also a plus—being able to manage large datasets with versioning and ACID support is honestly a lifesaver in production scenarios. **What do you dislike about Databricks?** The platform can feel a bit intimidating at first, especially if you're new to big data tools. Setting up clusters and understanding the pricing model took me a while. Also, the UI sometimes lags when you're dealing with large notebooks or switching between multiple tabs. I wish the onboarding was a bit more beginner-friendly **What problems is Databricks solving and how is that benefiting you?** Databricks helps us streamline our entire data pipeline — from ingestion to analytics to machine learning. Earlier, managing large-scale datasets and running ML models used to be fragmented across tools, but Databricks made it way smoother. It saves us a lot of dev time and reduces the friction between data engineering and data science teams. Having everything in one place also makes debugging and scaling much easier. **Official Response from Aunalisa Arellano:** > We're glad to hear that you're enjoying the comprehensive features of Databricks, including the seamless integration of data engineering, analytics, and machine learning. The collaborative notebooks and Delta Lake capabilities are designed to enhance productivity and efficiency. ### 25. Unlocking Scalable Data Insights with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** Abhi J. | Software Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** July 24, 2025 **What do you like best about Databricks?** Databricks excels in unifying data engineering, analytics, and machine learning in a collaborative, cloud-based environment. Its support for multiple programming languages (Python, SQL, Scala, R) makes it incredibly flexible. The Lakehouse architecture simplifies data management by combining the best of data lakes and data warehouses. The auto-scaling compute clusters, tight integration with tools like MLflow, and powerful notebooks streamline experimentation and production deployment. I also appreciate the frequent product updates and commitment to open-source technologies like Apache Spark and Delta Lake. **What do you dislike about Databricks?** While powerful, Databricks has a learning curve—especially for non-technical users or those new to Spark-based architectures. Pricing can escalate quickly if not closely monitored, particularly with always-on clusters. The UI, although improving, still feels unintuitive in certain areas (like managing jobs or cluster permissions). Some integrations, especially with on-premise systems, require additional effort or custom workarounds. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses the fragmentation between data engineering, data science, and analytics by offering a unified platform. Previously, we struggled with maintaining multiple disconnected tools for ETL, machine learning, and BI. Databricks' Lakehouse architecture allows us to manage structured and unstructured data in a single place, simplifying our data pipelines and reducing operational overhead. It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility. As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making. **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that you find our platform flexible, collaborative, and powerful. We appreciate your feedback and are committed to continuously improving the user experience. ### 26. Powerful Unified Platform for Data and AI, but Complex Setup and Costly for Continuous Use **Rating:** 5.0/5.0 stars **Reviewed by:** ILCHO I. | expert buyer, Airlines/Aviation, Enterprise (> 1000 emp.) **Reviewed Date:** October 16, 2025 **What do you like best about Databricks?** I like that Databricks provides a unified environment for data engineering, analytics, and machine learning. The platform makes it easy to collaborate across teams, manage large-scale data efficiently, and build advanced AI models using the same infrastructure. The integration with major cloud providers and the Lakehouse architecture make data management both flexible and scalable. **What do you dislike about Databricks?** While Databricks is a powerful and flexible platform, it can be complex to set up and manage, especially for teams without strong data engineering expertise. The cost structure can also become expensive for continuous workloads, and performance tuning sometimes requires deep knowledge of Spark and cluster optimization. Additionally, the user interface could be more intuitive for non-technical users. **What problems is Databricks solving and how is that benefiting you?** Databricks helps centralize and manage large volumes of data from different sources in a single, scalable platform. It simplifies data processing, analytics, and machine learning workflows, allowing teams to collaborate efficiently and deliver insights faster. By integrating data engineering, analytics, and AI capabilities, it reduces infrastructure complexity and accelerates the development of data-driven solutions. **Official Response from Aunalisa Arellano:** > We appreciate your positive feedback on Databricks' unified environment for data engineering, analytics, and machine learning. The integration with major cloud providers and the Lakehouse architecture are designed to provide flexibility and scalability in data management. ### 27. Worth the effort **Rating:** 5.0/5.0 stars **Reviewed by:** Shaurya J. | Marketing Manager, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** August 01, 2025 **What do you like best about Databricks?** Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline. **What do you dislike about Databricks?** While Databricks is incredibly powerful, the learning curve can be steep for non-technical users or teams new to distributed computing. The UI, though functional, can sometimes feel a bit clunky compared to more modern data platforms. Additionally, managing costs in a multi-user environment requires careful governance, especially for teams running large-scale compute-heavy jobs. **What problems is Databricks solving and how is that benefiting you?** Databricks is helping us break down data silos by centralizing data engineering, analytics, and machine learning into a unified environment. It simplifies handling large datasets, automates ETL processes, and enables real-time analytics and AI-driven insights. As a result, we’ve significantly improved our data pipeline efficiency, reduced time to insights, and empowered both data scientists and analysts to collaborate more effectively using a single platform. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks Data Intelligence Platform valuable for unifying data engineering, analytics, and machine learning. We appreciate your feedback on the learning curve and UI, and we're continuously working to improve the user experience. Thank you for sharing how Databricks is benefiting your team by centralizing data and improving efficiency. ### 28. Effortless Data Insights and Governance **Rating:** 4.5/5.0 stars **Reviewed by:** Firat S. | Data Analyst, Oil & Energy, Mid-Market (51-1000 emp.) **Reviewed Date:** January 13, 2026 **What do you like best about Databricks?** I like the Databricks Data Intelligence Platform for its data governance capabilities. The platform supports machine learning applications and offers helpful autofilling features. I also find the quick analytics code support to be a valuable aspect. **What do you dislike about Databricks?** I find it problematic that if the tables have two similar attributes and I need to choose another which isn't many-to-many, it can't handle that yet. Also, when I ask for the keys of a table, whether foreign or main, it's not able to provide the correct key. **What problems is Databricks solving and how is that benefiting you?** Databricks Data Intelligence Platform reduces the time to find relationships between tables. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with our platform's data governance and machine learning features. We understand the challenges you've mentioned and will work to enhance our capabilities in handling similar attributes and providing accurate table keys. ### 29. Effortless Workflow Management with Databricks Data Intelligence **Rating:** 4.5/5.0 stars **Reviewed by:** Nargis K. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** December 20, 2025 **What do you like best about Databricks?** As a tour and travels organization our goal is to provide seamless and fast experience to our customers. With Databricks Data Intelligence we can track and manage our internal workflow very efficiently, as it gives a very brief report our analytics. We often uses it to scale our business and security. It's UI is very clean and easy to understand dashboard gives us total freedom of our management. **What do you dislike about Databricks?** The cost is greater compared to similar platforms. **What problems is Databricks solving and how is that benefiting you?** In today's world, where AI is becoming increasingly prevalent, it offers many useful features that help save time and automate much of our work efficiently. **Official Response from Janelle Glover:** > It's great to hear that Databricks Data Intelligence Platform is helping you save time and automate your work efficiently in today's AI-driven world. Thank you for sharing your experience. ### 30. one of leaders in data **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** October 02, 2025 **What do you like best about Databricks?** What I really like about the Databricks Data Intelligence Platform is how it brings everything together in one place. Instead of juggling different tools for data engineering, analytics, machine learning, and governance, you can do it all in a single environment. **What do you dislike about Databricks?** Honestly, what I find a bit frustrating about the Databricks Data Intelligence Platform is that while it’s incredibly powerful, it can also feel overwhelming at times. There’s a steep learning curve, especially for teams who are just getting started and don’t have much experience with Spark or distributed systems. **What problems is Databricks solving and how is that benefiting you?** The big problem it solves is breaking down data silos. Instead of having separate systems for raw data, analytics, and machine learning, Databricks gives you one platform where everything connects. That means less time moving data around and more time actually using it. **Official Response from Aunalisa Arellano:** > We're glad to hear that you appreciate the all-in-one capabilities of our Data Intelligence Platform. We understand that the learning curve can be steep, and we're continuously working on improving our onboarding and support resources to help teams get up to speed more efficiently. ### 31. Empowering Data Teams with Unified Intelligence and Performance **Rating:** 4.5/5.0 stars **Reviewed by:** Laxman K. | Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 10, 2025 **What do you like best about Databricks?** Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably. **What do you dislike about Databricks?** While powerful, the platform has a learning curve—especially for teams unfamiliar with Spark or distributed computing. Some features (like Unity Catalog or serverless compute) can be region-specific or limited by cloud provider compatibility. Additionally, job debugging and cluster cost management can be challenging without careful monitoring and tagging, particularly in enterprise-scale projects. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the critical problem of data fragmentation by unifying data engineering, data science, analytics, and governance in one platform. Previously, we had to stitch together multiple tools—ETL frameworks, notebooks, ML platforms, and data warehouses. With Databricks, everything from ingestion to model deployment happens in one place, drastically reducing complexity and context-switching. Another key problem it addresses is scalability and performance for big data workloads. The platform’s native support for Apache Spark and Delta Lake enables reliable, fast processing of massive datasets. This helps us run ML pipelines and analytics at scale without worrying about infrastructure bottlenecks. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on Databricks Data Intelligence Platform! We're thrilled to hear that you appreciate our unified approach to data engineering, analytics, and AI/ML. We understand that the learning curve and region-specific limitations can be challenging, and we're continuously working to improve user experience and expand compatibility. Our team is dedicated to providing resources and support to help users navigate these complexities more easily. We would love to connect with you to address any specific concerns you have and explore how we can enhance your experience further. Please feel free to reach out to our support team for assistance. Thank you for choosing Databricks to streamline your data processes and empower your team! ### 32. Excellent ML Features and Data Controls in Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Aashu V. | Data Engineering Manager, Enterprise (> 1000 emp.) **Reviewed Date:** January 08, 2026 **What do you like best about Databricks?** Databricks offers useful features for machine learning engineers, such as the ability to use compute pools to run workloads efficiently. In addition, it includes the Unity Catalog, which is an excellent tool for managing data access controls. **What do you dislike about Databricks?** The product would benefit from having more built-in functions that are specifically optimized for GenAI use cases. **What problems is Databricks solving and how is that benefiting you?** We successfully consolidated data from multiple ERPs onto a single platform, which enabled us to use this unified data for data engineering, reporting, machine learning, and GenAI use cases. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks' ML features and data controls useful for your work. We appreciate your feedback about the need for more built-in functions and will take it into consideration for future updates. ### 33. Databricks - Powerful and scalable for data science and business analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Geral G. | Senior Consultant, Small-Business (50 or fewer emp.) **Reviewed Date:** May 28, 2025 **What do you like best about Databricks?** I like it because it stands out for its ability to unify data science, data engineering, and business analytics into a single interface. I also appreciate the seamless integration with collaborative notebooks and the ability to seamlessly with Delta Lake is also a big plus, ensuring reliability and performance when managing large-scale data. **What do you dislike about Databricks?** Sometimes, the web interface can take a while to load active clusters. Id also like more visual tools to monitor resource usage in real time. **What problems is Databricks solving and how is that benefiting you?** One of the main challenges we faced was fragmentation between engineering and analytics teams, and Databricks allowed us to centralize all data processing and analysis, reducing development time and improving the quality of our machine learning models. We also eliminated data sites by consolidating multiple sources into a single platform, which reduced the time to insights and the execution time of ETL pipelines, improved collaboration and overall productivity, and improved business decision supported by cleaner data. **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks Data Intelligence Platform! We're thrilled to hear that you find the platform unifying and efficient for your data science and analytics needs. We appreciate your feedback regarding the web interface loading times and the need for more real-time monitoring tools. Your insights are valuable to us as we continuously strive to enhance our platform. We'll make sure to pass along your suggestions to our product development team. If you have any further feedback or need assistance, please feel free to reach out. We're here to support you every step of the way. ### 34. Great Experience with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** Brandon C. | Director, Data Science and Analytics, Enterprise (> 1000 emp.) **Reviewed Date:** May 28, 2025 **What do you like best about Databricks?** The Databricks Data Intelligence Platform allows us to have a single source of development capabilities for IT developers and business analysts. This allows for easier implementation of capabilities and consolidation of toolsets across the environment. Users are given the freedom to develop the data products they need in the time frames they need them. It makes implementation and productionalization of these projects much easier to integrate for downstream usage. **What do you dislike about Databricks?** The one "dislike" I have is that it's difficult to keep up with all of the improvements and enhancements in the platform. We always see new features to implement and want to make sure we're doing the best we can for our end users. **What problems is Databricks solving and how is that benefiting you?** Databricks is making it extremely easy for us to have a single set of tooling for multiple us cases in the data space. This allows us to be more collaborative across business units and within business units. It also allows us to have standard ways of working that can be implemented through all of the use cases. We see a lot of capabilities that can be leveraged with the platform that make repeatable capabilities easier which allows us to increase speed to market for our data products **Official Response from Aunalisa Arellano:** > We're thrilled to hear that you are enjoying the benefits of the Databricks Data Intelligence Platform, especially the ease of development and consolidation of toolsets. We understand that keeping up with new features can be challenging, and we're continuously working to improve our communication and support for our users. ### 35. Power of lakehouse to support AI **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Enterprise (> 1000 emp.) **Reviewed Date:** September 09, 2025 **What do you like best about Databricks?** The Databricks Data Intelligence Platform is a unified, AI-native environment that brings together data engineering, analytics, governance, and machine learning on top of the Lakehouse architecture. Its strength lies in combining open data formats with centralized governance via Unity Catalog and embedding intelligence through DatabricksIQ, which allows enterprises to securely connect their data with large language models. From an evaluation standpoint, the platform’s value is in enabling organizations to not only manage and analyze data at scale but also to operationalize generative AI use cases in a governed and collaborative manner. **What do you dislike about Databricks?** Current stage of evolving technology may require rebuilding on future if adapted by enterprise **What problems is Databricks solving and how is that benefiting you?** Across industries, the platform solves problems like fraud detection, personalization, compliance, predictive analytics, and AI-driven customer engagement—all by combining data unification, governance, and machine learning in one environment. **Official Response from Aunalisa Arellano:** > Thank you for sharing your evaluation of the platform's value in enabling organizations to manage and analyze data at scale and operationalize generative AI use cases in a governed and collaborative manner. ### 36. Impressive AI Features and Unified Lakehouse Architecture **Rating:** 4.5/5.0 stars **Reviewed by:** Samridhi J. | AI Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** January 07, 2026 **What do you like best about Databricks?** The recent AI features are quite helpful and also the unified lakehouse architecture. **What do you dislike about Databricks?** A bit tough for those new to spark or from a non tech background especially the table view. **What problems is Databricks solving and how is that benefiting you?** unified data platform for all my data from gsc to bigquery to data warehouse and datalake. Super high processing speed. Easy to use and understand with a clean UI. the AI query mode is very helpful and kind of addictive lol. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with our AI features and unified lakehouse architecture. We understand the challenges for users new to Spark or from a non-tech background, and we're actively working on improving the user interface and experience to make it more intuitive for all users. ### 37. Databricks is a Powerful Platform with Strong Support and Flexible Features **Rating:** 5.0/5.0 stars **Reviewed by:** Sean H. | Head of Operations, Small-Business (50 or fewer emp.) **Reviewed Date:** May 30, 2025 **What do you like best about Databricks?** Databricks is very dependable, flexible, and helps our company to create innovative analytical solutions and every week in our technical meetings, we cover a range of subjects like bugs, best practices, new features, and more using it. Also every member of the support crew in Databricks responds fast and is rather helpful. **What do you dislike about Databricks?** When using creative features like AI, cost control and estimation might prove difficult. And although lakeview SQL is still not yet developed, Databricks is actively pushing their utilization nonetheless. Also every now and then unannounced feature activation in my office surprises me. **What problems is Databricks solving and how is that benefiting you?** Databricks is a must have tool for managing complex data access and it's innovative infrastructure which guarantees fast scalability and offers advanced capabilities and also one can easily combine other solutions with it and right now, we use it as a one-stop shop to satisfy several audiences. **Official Response from Aunalisa Arellano:** > We're glad to hear that Databricks has been dependable, flexible, and helpful in creating innovative analytical solutions for your company. Our support team is committed to responding quickly and providing valuable assistance. ### 38. Powerful EDA Tool with Interactive Notebooks **Rating:** 4.0/5.0 stars **Reviewed by:** Aditya V. | Associate Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** December 27, 2025 **What do you like best about Databricks?** I like its usable interface and the interactive notebooks that support SQL and Python for visualization in one place. **What do you dislike about Databricks?** For learners it may be difficult for beginners and cost may rise quickly **What problems is Databricks solving and how is that benefiting you?** I use Databricks Data Intelligence Platform for EDA on large datasets. It processes large sets of data for analysis, and I like its usable interface and interactive notebooks that support SQL and Python for visualization. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks Data Intelligence Platform benefits your work in processing and analyzing large datasets. We understand that the learning curve and potential cost can be concerns for beginners. We're constantly working to improve our platform and offer resources to help users get started. ### 39. Report 1100 **Rating:** 5.0/5.0 stars **Reviewed by:** Farzad E. | CyberSecurity Expert, Enterprise (> 1000 emp.) **Reviewed Date:** January 16, 2026 **What do you like best about Databricks?** I started with databricks 6 years ago and received more than 10 certifications. I liked a lot data analytics and fast calculations features of databricks. As well integration to other external tools like Power BI for reporting. **What do you dislike about Databricks?** All features were fine, but more AI-Powered features need to enhace all current features. **What problems is Databricks solving and how is that benefiting you?** Analytics, fast calculations and reporting. **Official Response from Janelle Glover:** > Thank you for being a long-time user of Databricks and for sharing your positive experience with us! We're glad to hear that you appreciate the data analytics and fast calculations features, as well as the integration with external tools like Power BI. We'll definitely take your feedback about enhancing AI-powered features into consideration for future improvements. ### 40. Data Representation & Management Tool **Rating:** 5.0/5.0 stars **Reviewed by:** Caleigh H. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** May 24, 2025 **What do you like best about Databricks?** Working on the Databricks where we can easily analyze huge datasets and integrate our platform or website to create an insight from our internal dataset. They have number of features that help us to manage all analytical views with pre define templates. **What do you dislike about Databricks?** Implementation was so quick and easy that help us to manage all data without any issues. I like their customer support services and their frequent use make them my favorite platform for data management. **What problems is Databricks solving and how is that benefiting you?** I have required a tool that help me to represent and analyze the data and I use Databricks and really their excellent platform make my work more reliable and easy to manageable for me. i always prefer them their pricing is little bit high but their services gives a big impact in my work. **Official Response from Aunalisa Arellano:** > Thank you for your positive feedback! We're glad to hear that the quick and easy implementation, along with our customer support services, have made Databricks your favorite platform for data management. ### 41. Collaborative Notebooks and MLflow Integration Boost Productivity **Rating:** 3.5/5.0 stars **Reviewed by:** Umair A. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** December 22, 2025 **What do you like best about Databricks?** The collaborative notebooks are quite useful. My team is able to write both Python and SQL within the same notebook, which really helps speed up our development cycle. The integration with MLflow is, in my opinion, the standout feature. **What do you dislike about Databricks?** There was nothing I disliked; everything worked as expected. **What problems is Databricks solving and how is that benefiting you?** We utilized this tool to develop predictive models aimed at optimizing our supply chain. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of collaborative notebooks and our MLflow integration. We're thrilled to hear that these features are helping to optimize your supply chain through predictive modeling. ### 42. The best analytical and data governing platform **Rating:** 4.5/5.0 stars **Reviewed by:** yuvraj m. | Data engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2025 **What do you like best about Databricks?** The best I like about snaplogic is while doing data transformation of datasets in notebooks it has many languages to use like sql,pyspark and R. This enable me to use any language and effectively complete my requirements.data bricks has many features like Ai,unity catalog workflow managements which makes my ETL implementation ,and data integration easy and quick **What do you dislike about Databricks?** Under heavy usage the UI responsive for some implementations like job tracking ,Notebook versioning is a bit slugguish on web **What problems is Databricks solving and how is that benefiting you?** The problems databricks solving is effective datasets to data product creation satisfying business requirements **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks Data Intelligence Platform helpful for your data transformation needs with its support for multiple languages like SQL, PySpark, and R. ### 43. Unity Catalog streamlines efficiency, despite Compute mode limitations **Rating:** 4.0/5.0 stars **Reviewed by:** Kollen G. | Data Engineer/Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 10, 2025 **What do you like best about Databricks?** The Unity Catalog bundles useful data types together like Tables (delta), Models, Views, Functions and Volumes (for unstructured data). Additionally, the Workflows tab streamlines efficiency to run jobs and pipelines updating and engaging with big data. **What do you dislike about Databricks?** The worst functionality comes with the compute modes, and the restricted abilities. For example, Dedicated single-user access mode is essential to utilize ML runtimes and some Spark context access; however, Standard shared access mode is key for the latest UC functionality such as engaging with Shallow Clones properly. **What problems is Databricks solving and how is that benefiting you?** Problems with managing big data and streamlining execution time while also managing permissions and governance are all problems that are efficiently solved using Databricks Data Intelligence Platform and Unity Catalog. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find the Unity Catalog and Workflows tab helpful for bundling data types and streamlining efficiency. ### 44. Excellent Experience with Databricks: Enabling efficient data analytics and insights **Rating:** 5.0/5.0 stars **Reviewed by:** Elizabeth A. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 11, 2025 **What do you like best about Databricks?** Databricks’ platform is a powerful collaborative tool which allows teams to work together seamlessly on data projects. The integrated environment for data processing and analytics, along with its user-friendly interface, makes it easy to visualize insights and share findings in real time. **What do you dislike about Databricks?** The occasional complexity in managing multiple clusters and environments, which can lead to confusion regarding resource allocation. Additionally, the learning curve for new users can be steep, making it a challenge for organizational adoption. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the challenges of data integration and analysis by providing a unified platform that streamlines data processing and collaboration. This is enabling quicker access to insights from diverse data sources, improving productivity and decision making. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks is solving your data integration and analysis challenges, enabling quicker access to insights and improving productivity. We appreciate your input and will continue to focus on streamlining data processing and collaboration. ### 45. It was fantastic got to meet many peers **Rating:** 3.5/5.0 stars **Reviewed by:** Sai P. | Azure Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 12, 2025 **What do you like best about Databricks?** I attended to explore how Databricks unifies data engineering, analytics, and AI. The platform’s integration with BI tools and support for Delta Lake worked well. Performance and collaboration features stood out. However, the learning curve and some UI complexities could be improved for newer users transitioning from traditional platforms. **What do you dislike about Databricks?** I attended the Databricks Data Intelligence Platform session but found it rather unremarkable. The features and performance were neither outstanding nor disappointing. It felt generic, with vague improvements and minimal innovation. Overall, I am uncertain about its distinct advantages, rendering my review neither particularly informative nor actionable for prospective users. **What problems is Databricks solving and how is that benefiting you?** 1. Data Silos • Brings data from multiple sources (structured, unstructured, streaming) into a unified platform for centralized access. 2. Slow Time to Insights • Enables faster data processing with Spark, Delta Lake, and optimized workflows to accelerate decision-making. 3. Scalability Issues • Handles growing data volumes and user loads efficiently with cloud-native, distributed compute. **Official Response from Aunalisa Arellano:** > Thank you for taking the time to share your thoughts on the Data Intelligence Platform and your experience at the Data + AI Summit. We appreciate your feedback and will definitely share it it with the team and consider it when developing content for future events. ### 46. Powerful unified data platform that transformed our analytics workflow **Rating:** 5.0/5.0 stars **Reviewed by:** Abdullah A. | Director, Databricks Solutions, Chemicals, Mid-Market (51-1000 emp.) **Reviewed Date:** June 12, 2025 **What do you like best about Databricks?** What I appreciate most about Databricks is its unified approach to data engineering and data science. The platform eliminates the traditional silos between our data engineers and data scientists by providing a collaborative workspace where both teams can work on the same datasets using their preferred tools - whether that's Spark, Python, R, or SQL. The Delta Lake technology has been particularly valuable for ensuring data quality and reliability in our pipelines. The auto-scaling clusters mean we don't have to worry about infrastructure management, and the notebook interface makes it easy to document and share our work. MLflow integration for experiment tracking and model deployment has streamlined our machine learning lifecycle significantly **What do you dislike about Databricks?** The main challenges we've encountered are around the learning curve and cost management. For team members coming from traditional SQL backgrounds, the transition to Spark-based analytics requires significant upskilling. The pricing model can be complex to predict, especially with auto-scaling clusters, and costs can escalate quickly if not monitored carefully. The UI, while functional, can feel overwhelming for new users with so many features and options. We've also experienced occasional performance inconsistencies during peak usage times, and some of the more advanced features require deep technical knowledge to implement effectively. Documentation, while comprehensive, can be dense and assumes a high level of technical expertise. **What problems is Databricks solving and how is that benefiting you?** Slow Time-to-Insight: Our analytics queries that previously took hours now complete in minutes, enabling faster business decision-making. Data Infrastructure Complexity: We've eliminated the need to manage separate systems for data processing, storage, and ML, reducing operational overhead and technical debt. Cross-Team Collaboration Barriers: Data engineers and data scientists now work in the same environment, improving project velocity and reducing miscommunication. Scalability Bottlenecks: The platform automatically scales to handle peak workloads without manual intervention, supporting our growing business needs. ML Model Governance: MLflow provides proper versioning, tracking, and deployment capabilities for our machine learning initiatives, ensuring models can be reliably moved to production. These solutions have resulted in measurable business impact including reduced operational costs, faster product development cycles, and more data-driven decision making across the organisation. **Official Response from Aunalisa Arellano:** > We're pleased to hear that Databricks has helped to address your organization's challenges, resulting in faster time-to-insight, simplified data infrastructure, improved cross-team collaboration, seamless scalability, and enhanced ML model governance. We're committed to continuing to support your business needs and are glad to hear about the measurable impact on your organization. ### 47. All in one place for all data needs **Rating:** 4.5/5.0 stars **Reviewed by:** Viraj R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 10, 2025 **What do you like best about Databricks?** Honestly, it’s how it turns the chaos of raw data into something beautifully orchestrated—like jazz, but with less saxophone and more Spark. I love that it’s not just a tool—it’s an entire ecosystem. It brings data engineering, analytics, and AI into one unified environment. That means I don’t have to duct tape a dozen tools together just to move a pipeline forward. Fewer silos, more flow. And the fact that it’s built on open standards like Delta Lake and Apache Spark? Chef’s kiss. The collaborative features are also huge. Being able to work seamlessly across teams in notebooks, share insights, and iterate in real time? It’s like Google Docs for data nerds, but smarter and with fewer formatting headaches. Oh, and Unity Catalog? Game-changer. It gives me visibility and governance without making me feel like I need a second job in security. So yeah—what I like most is that it makes doing complex things feel, well… less complex. It’s not just about managing data—it’s about getting real intelligence out of it, fast. And that’s exactly the kind of velocity I like. **What do you dislike about Databricks?** First off, pricing transparency can be… let’s call it “mysterious.” You think you’re just running a simple job, and then BAM—surprise costs like it’s your birthday, but the gifts are all invoices. I’d love a bit more clarity and control there, especially when you’re trying to scale responsibly. **What problems is Databricks solving and how is that benefiting you?** Certificate related parsing with great speed **Official Response from Aunalisa Arellano:** > Jazz with less sax and more Spark is what we are all about! ;) Thank you so much for taking the time to leave a thoughtful review on Databricks! We love to hear that Unity Catalog is a game-changer for your organization. Thank you for the feedback on pricing. This is something we will relay to the team. ### 48. Game changer **Rating:** 5.0/5.0 stars **Reviewed by:** Sachin V. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 25, 2025 **What do you like best about Databricks?** This lake house architecture brings the best of both data lakes and warehouses , so we don't have to deal with the unnecessary complexity. Delta lakes ensures reliability while the notebook based interface makes collaboration seamless. The platform ability to handle batch, streaming, and machine learning workloads in one place is a huge advantage. **What do you dislike about Databricks?** The pricing can get expensive, especially if workloads aren't optimized properly, also while the notebooks are great they could use better version control for collaborative work . the initial learning curve can be a bit steep for those new to spark but once you get the hang of it. It is powerful tool **What problems is Databricks solving and how is that benefiting you?** Databricks simplifies big data processing by providing a unified platform for batch and streaming workloads. It eliminates the complexity of managing infrastructure, ensuring scalability and performance without much manual effort . The collaborative notebooks make it easy to work with items and delta Lake improves data reliability. overall it saves a lot of time and effort in managing data workloads **Official Response from Aunalisa Arellano:** > We're glad to hear that you find our lakehouse architecture and Delta Lakes reliable and seamless for collaboration. Thank you for sharing your experience with us. ### 49. Databricks is a great solution for data engineering and analytics **Rating:** 4.5/5.0 stars **Reviewed by:** Dakota R. | Senior Technical Program Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** March 22, 2025 **What do you like best about Databricks?** I love Databricks because it consolidates data engineering, machine learning and analytics into one and the feat of using collaborative notebooks also enables real-time and seamless teamwork in working between a data engineer working on data pipelines and a data scientist running various experiments. And it handles large-scale data quite easily and running complex SQL queries within seconds with no infrastructure related issues. Also it has some built-in governance tools like unity catalog which help me in managing data lineage and controlling access. **What do you dislike about Databricks?** Databricks can be quite overwhelming for beginners especially if they do not have a good grasp of sql and spark to begin with. And the pace of their updates, although is quite good, it tends to introduce breaking changes which can be a pain to keep up with. Also the pricing can get expensive at scale especially for teams that work with really big sets of data. **What problems is Databricks solving and how is that benefiting you?** Before Databricks, we had disjointed processes, terribly slow data processing and no easy way to collaborate but now, we can integrate multiple data sources more easily and process big data in an efficient manner. Also this rapid development of machine learning models makes the infrastructure manageable for us and we save hundreds of hours with automation – not just in maintaining infrastructure but in thinking innovation first. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks has helped you integrate multiple data sources more easily, process big data efficiently, and develop machine learning models rapidly. We're committed to providing solutions that save time and enhance innovation for our users. ### 50. Databricks has opened a world of possibilities for me **Rating:** 5.0/5.0 stars **Reviewed by:** Isaias G. | CEO & Founder, Mid-Market (51-1000 emp.) **Reviewed Date:** March 20, 2025 **What do you like best about Databricks?** Databricks is a tool that is constantly evolving. I really appreciate the number of features they release, and the fact that they are up to date. It's an everyday platform that adapts to all use cases and is easy to integrate. It's an all-in-one solution with ETL with warehouses, Python, Spark, Scala, machine learning, and GenAI. **What do you dislike about Databricks?** Sometimes I wish they would update the documentation more, that there weren't so many changes in the API . The release notes of the changes were better explained to us and that we would have time to address the changes. Customer support is one thing that they have to improve too **What problems is Databricks solving and how is that benefiting you?** With Unity Catalog we have been able to address many issues we had with the previous integration with Glue and AWS, now everything is simpler and we can have more granularity in our security and ease in our processes. **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks and the benefits it has brought to your processes. We understand your concerns about the documentation, API changes, and customer support, and we will work on addressing these areas for a better user experience. ## Databricks Discussions - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [View Databricks pricing details and edition comparison](https://www.g2.com/products/databricks/reviews?filters%5Bsentiment_snippet%5D=1559079&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-12+22%3A23%3A08+-0500&secure%5Bsession_id%5D=59d54397-c9c4-4bb2-847b-607c1f00a4f7&secure%5Btoken%5D=6744bf66509b572df3d52ec56592ffb4eac36330a927c3ddee9ba8b3cb962c67&format=llm_user) ## Databricks Integrations - [Agentforce Sales (formerly Salesforce Sales Cloud)](https://www.g2.com/products/agentforce-sales-formerly-salesforce-sales-cloud/reviews) - [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) - [Amazon Relational Database Service (RDS)](https://www.g2.com/products/amazon-relational-database-service-rds/reviews) - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews) - [Anaplan](https://www.g2.com/products/anaplan/reviews) - [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews) - [Apache Kafka](https://www.g2.com/products/apache-kafka/reviews) - [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - [AWS Lambda](https://www.g2.com/products/aws-lambda/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews) - [Azure Data Lake Store](https://www.g2.com/products/azure-data-lake-store/reviews) - [Azure DevOps Server](https://www.g2.com/products/azure-devops-server/reviews) - [Azure Logic Apps](https://www.g2.com/products/azure-logic-apps/reviews) - [Azure OpenAI Service](https://www.g2.com/products/azure-openai-service/reviews) - [Azure Pipelines](https://www.g2.com/products/azure-pipelines/reviews) - [Azure Portal](https://www.g2.com/products/azure-portal/reviews) - [Azure SQL Database](https://www.g2.com/products/azure-sql-database/reviews) - [Base SAS](https://www.g2.com/products/base-sas/reviews) - [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) - [Claude Code](https://www.g2.com/products/anthropic-claude-code/reviews) - [Crunchbase](https://www.g2.com/products/crunchbase/reviews) - [Dash](https://www.g2.com/products/dash-for-brands-ltd-dash/reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [DigitalOcean](https://www.g2.com/products/digitalocean/reviews) - [Domo](https://www.g2.com/products/domo/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - 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[Microsoft SharePoint](https://www.g2.com/products/microsoft-sharepoint/reviews) - [Microsoft SQL Server](https://www.g2.com/products/microsoft-sql-server/reviews) - [Microsoft Teams](https://www.g2.com/products/microsoft-teams/reviews) - [MLflow](https://www.g2.com/products/mlflow-mlflow/reviews) - [MySQL](https://www.g2.com/products/mysql/reviews) - [ObjectWay SpA](https://www.g2.com/products/objectway-spa/reviews) - [Pega Platform](https://www.g2.com/products/pega-platform/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [PowerBI Portal](https://www.g2.com/products/powerbi-portal/reviews) - [Prophecy](https://www.g2.com/products/prophecy-prophecy/reviews) - [Salesforce Agentforce](https://www.g2.com/products/salesforce-agentforce/reviews) - [Salesforce Headless 360 Platform (formerly Salesforce Platform)](https://www.g2.com/products/agentforce-360-platform-formerly-salesforce-platform/reviews) - [SAP Ariba](https://www.g2.com/products/sap-ariba/reviews) - [SAP ECC](https://www.g2.com/products/sap-ecc/reviews) - [Seamless (formally Seamless.AI)](https://www.g2.com/products/seamless-formally-seamless-ai/reviews) - [ServiceNow IT Service Management](https://www.g2.com/products/servicenow-it-service-management/reviews) - [Sisense](https://www.g2.com/products/sisense/reviews) - [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) - [Spark SQL](https://www.g2.com/products/spark-sql/reviews) - [Spotfire Analytics](https://www.g2.com/products/spotfire-analytics/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [Unity Catalog Command Center](https://www.g2.com/products/unity-catalog-command-center/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) - [Workday HCM](https://www.g2.com/products/workday-hcm/reviews) ## Databricks Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards - Customizable Reports - Marketing Reports - Sales Reports - Activity Dashboard - Interactive Reports - Customizable Reports - Customizable Reports - Activity Dashboard - Customizable Dashboard **Additional Functionality** - Continuous Integration - Drag & Drop - Backup and Recovery - Multiple Programming Languages Supported - Continuous Deployment - Code Development - For No-Code Development - Version Control - Configurable Workflow - Graphical User Interface - For Low-Code Development - Activity Dashboard - Generative AI - UI Prototyping - Source Control - Software Development - API - Data Import/Export - Web App Development - Custom Development - Code Repository Integration - Data Security - Mobile Development - Access Controls/Permissions - Game Development - Collaboration Tools - Application Security - Alerts/Notifications - Automated Testing - Integrated Development Environment - Offline Access - Debugging - AI Copilot - Reporting/Analytics - Compatibility Testing - Pre-built Templates - Third-Party Integrations - Data Modeling - Customizable Branding - Data Visualization - Code Editing **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Data Cleansing - Data Extraction - Third-Party Integrations - Data Verification - Information Governance - Data Capture and Transfer - AI Copilot - Customizable Reports - Data Migration - Behavior Analytics - Real-Time Reporting - Interactive queries - Audit Trail - Multiple Data Sources - Document Storage - Access Controls/Permissions - User Management - AI/Machine Learning - Master Data Management - Task Scheduling - Data Import/Export - Monitoring - Customer Database - Audit Management - Reporting/Analytics - Data Visualization - Collaboration Tools - Workflow Management - Full Text Search - Compliance Management - Generative AI - Data Quality Control - Data Connectors - Automatic Backup - Activity Tracking - Activity Dashboard - Data Security - Data Analysis Tools - Data Synchronization - Metadata Management - Dashboard Creation - Visual Analytics - Secure Data Storage - Data Integration - Database Support **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations - Configurable Workflow - Rules-Based Workflow **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling - Real-Time Data **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics - ETL - Extract Transfer Load - Data Capture and Transfer - Real-Time Analytics - Reporting/Analytics **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage - Data Visualization **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Reports** - Reports Interface - Share Reports - Steps to Answer **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Functionality** - Ease of Use - File Management - Multi-Language Support - Customization - Straight-Out-the-Box Functionality - Help Guides - Patching & Updates - Workflow Management - Change Management - Deployment Management - Performance Management - Compliance Management - Lifecycle Management - Task Management - Document Management - Database Support **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Customization - AI Agent Builders** - Natural Language Configuration - Tone Customization - Security Guardrails - API Security - Data Security - Authentication **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Traffic Management & Performance - AI Gateways** - Token-Aware Rate Limiting - Semantic Caching - Multi-Model Routing & Fallbacks **Configuration** - Application Performance - Orchestration - Database Monitoring - Anomaly Detection - Network Security **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training - Database Support - Multi-Language **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying - Reporting/Analytics - Predictive Analytics - Visual Analytics **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring - Real-Time Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability - Non-Relational Transformations - Data Extraction **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending - Data Capture and Transfer **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Security** - Access Control - Roles Management - Compliance Management - Deletion Management - Access Controls/Permissions - User Management - Process Management - Audit Management - Metadata Management **Operations** - Metrics - Infrastructure management - Collaboration **Visualization** - Graphs and Charts - Score Cards - Dashboards - Formats - Mobile Dashboards - Public Dashboards - Private Dashboards **Analytics** - Analytics capabilities - Dasboard visualizations **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Functionality - AI Agent Builders** - Omni-channel Support - Agent Branding - Proactive Response Capabilities - Seamless Human Escalation - Multimedia Support - Multi-Modal Input Support **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Additional Functionality** - Data Mapping - Monitoring - Charting - Integration Management - Reporting/Analytics - Ad hoc Analysis - Access Controls/Permissions - API - Match & Merge - Real-Time Monitoring - Metadata Management - Pipeline Management - Job Scheduling - Dashboard Creation - Data Storage Management - Multiple Data Sources - Data Import/Export - Generative AI - Data Quality Control - Data Connectors - Customizable Reports - Single Sign On - Version Control - Visual Analytics - Accounting Integration - Real-Time Data - eCommerce Management - AI Copilot - CRM - Data Visualization - SSL Security - Search/Filter - Real-Time Analytics - Data Capture and Transfer - Collaboration Tools - Performance Management - Data Synchronization - Drag & Drop - Data Replication - Activity Dashboard - Database Support - Workflow Management - Alerts/Notifications - Predictive Analytics - Data Migration - Third-Party Integrations - Reporting & Statistics - Data Analysis Tools **Database Administration** - Provisioning - Governance - Auditing **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Integrations** - Hadoop Integration - Spark Integration **Data Quality** - Data Preparation - Data Distribution - Data Unification **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Deployment** - On-Premise - Cloud **Maintainence** - Data Quality Management - Policy Management - Data Storage Management **Management** - Cataloging - Monitoring - Governing **Data Updates** - Historical Snapshots - Real-Time Updating **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Data and Analytics - AI Agent Builders** - Analytics & Reporting - Contextual Awareness - Data Privacy Compliance **Availability** - Scalability - Backup - Archiving - Indexing **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake - Real-Time Data - Data Capture and Transfer - Trend Analysis - What-if Analysis - Statistical Analysis - Data Blending - Ad hoc Analysis - Third-Party Integrations **Security** - Data Masking - Authentication And Single Sign-On - Data Anonymization **Performance ** - Scalability **Collaboration** - Sharing - Co-Editing - Devices **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Integration - AI Agent Builders** - Workflow Automation - API Usage - Platform Interoperability - CRM Data Integration - Third-Party Integrations **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Additional Functionality** - Customizable Reports - Collaboration Tools - Data Extraction - Semantic Search - Data Storage Management - Ad hoc Reporting - Reporting/Analytics - Predictive Analytics - Activity Dashboard - Access Controls/Permissions - Visual Analytics - Data Mapping - Data Synchronization - Statistical Analysis - Categorization/Grouping - Trend Analysis - Data Profiling - Linked Data Management - Data Visualization - API - Multiple Data Sources - Sentiment Analysis - Search/Filter - Data Import/Export - Data Capture and Transfer - AI Copilot - Monitoring - Data Connectors - Ad hoc Analysis - Text Mining - Reporting & Statistics - Predictive Modeling - Real-Time Analytics - Configurable Workflow - Tagging - Endpoint Management - No-Code - Data Preparation - Auditing - Big Data Analytics - ML Algorithm Library - Data Management - Activity Tracking - Data Security - Workflow Management **Additional Functionality** - Version Control - Scalability - Personalization - Data Extraction - Webhooks - API - Natural Language Processing - Fallback Handling - Drag & Drop - Multiple LLM Models - Built-in AI Assistant - Automated Testing - Data Governance - Collaboration Tools - Pre-built Templates - Agent Design Tools - Deep Learning - Model Training - Analytics - Single Sign On - Debugging - Deployment Management - Proactive Error Detection **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling - Natural Language Search - Visual Discovery - Data Blending - Data Blending **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks - Data Discovery **Data Management** - Data Replication - Advanced Data Analytics **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image - Generative AI **Generative AI** - AI Text Generation - AI Text Summarization **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Governance** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Decision Making - Third-Party Integrations - Active Directory Integration **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity - Multiple Data Sources - Query Builder **Additional Functionality** - AI Copilot - Reporting & Statistics - Data Migration - Archiving & Retention - Data Capture and Transfer - Audit Trail - Data Synchronization - Single Sign On - Customizable Reports - Data Profiling - Authentication - Role-Based Permissions - Tagging - Secure Data Storage - Data Security - HIPAA Compliant - SSL Security - Risk Assessment - Data Mapping - Search/Filter - Self Service Portal - Multiple Data Sources - Document Storage - API - Activity Tracking - Automatic Backup - Visual Analytics **Additional Functionality** - Data Warehousing - Drag & Drop - Activity Dashboard - Data Connectors - Ad hoc Reporting - Customizable Reports - Alerts/Escalation - Templates - Forecasting - Data Migration - Data Transformation - Access Controls/Permissions - API - Data Cleansing - Data Synchronization - AI Copilot - Dashboard Creation - Data Extraction - Data Security - SSL Security - Collaboration Tools - No-Code - High Volume Processing - Database Support - Search/Filter - Generative AI **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services - Real-Time Analytics - Reporting/Analytics - Real-Time Analytics - Reporting/Analytics - Visual Analytics **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support - Database Support **Additional Functionality** - Parallel Processing - Ad hoc Analysis - Multiple Data Sources - API - In-Database Processing - Monitoring - Real-Time Reporting - Data Synchronization - Real-Time Monitoring - Data Connectors - Performance Metrics - Ad hoc Reporting - Alerts/Notifications - Access Controls/Permissions - Drag & Drop - Data Visualization - Data Import/Export - Match & Merge - Data Transformation - Secure Data Storage - Data Extraction - AI Copilot - Customizable Reports - Activity Dashboard - Data Migration - In-Memory Processing - Data Mapping **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - Auto-generated Insights and Narratives - Natural Language Queries - Proactive KPI Monitoring and Alerts - AI Agents for Analytical Follow-ups **Personalized Intelligence - Analytics Platforms** - Behavioral Learning for Contextual Query Refinement - Role-based Insight Personalization - Conversational and Prompt-based Analytics - Ad hoc Query - Visual Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management **Data Updates** - Historical Snapshots - Real-Time Updating - Scheduled/Automated Reports - Customizable Reports - Predictive Analytics - Data Management - Real-Time Data **Additional Functionality** - Drag & Drop - Secure Data Storage - Data Import/Export - Customizable Branding - Dashboard Creation - Access Controls/Permissions - Real-Time Reporting - Metadata Management - Forecasting - Data Storage Management - Ad hoc Analysis - Ad hoc Reporting - Data Migration - AI Copilot - Self Service Data Preparation - Search/Filter - Data Mapping - Monitoring - Sentiment Analysis - User Management - Sales Trend Analysis - Charting - Reporting & Statistics - Performance Metrics - Alerts/Notifications - Data Extraction - Widgets - Storytelling - Trend Analysis - Collaboration Tools - Financing Management - Self-service Analytics - KPI Monitoring - Dashboard - API - Data Integration - Scorecards - Data Management - Task Management - Progress Tracking - Data Connectors - Data Visualization - Profitability Analysis - Project Tracking - Strategic Planning - Goal Setting/Tracking - Publishing/Sharing - Predictive Analytics - Real-Time Monitoring - Templates - Mobile Access - Customizable Templates - Workflow Management - Financial Reporting - Audit Management - OLAP - Single Sign On - Data Synchronization **Additional Functionality** - Customizable Branding - Natural Language Processing - Data Extraction - AI Copilot - Ad hoc Reporting - Real-Time Reporting - Publishing/Sharing - Collaboration Tools - Strategic Planning - Self Service Data Preparation - Trend Analysis - Text Analysis - Real-Time Monitoring - Data Synchronization - Widgets - Benchmarking - Performance Metrics - Data Mapping - Generative AI - Trend/Problem Indicators - Customizable Templates - Access Controls/Permissions - Data Import/Export - Profitability Analysis - OLAP - Alerts/Notifications - Search/Filter - Drag & Drop - Data Connectors - Multiple Data Sources - Key Performance Indicators - Dashboard Creation - Role-Based Permissions - Data Mining - Forecasting - Ad hoc Query **Additional Functionality** - KPI Monitoring - Secure Data Storage - Multiple Data Sources - Data Visualization - Single Sign On - Natural Language Search - Search/Filter - Alerts/Notifications - AI Copilot - Real-Time Notifications - Collaboration Tools - Widgets - Performance Metrics - Ad hoc Reporting - Activity Tracking - Reporting/Analytics - Customizable Branding - Data Aggregation - Dashboard Creation - Data Connectors - Customizable Templates - Data Synchronization - API - Forecasting - Interactive Elements - Single Page View - Data Import/Export - Drag & Drop - Access Controls/Permissions - Trend Analysis - Workflow Management - Third-Party Integrations - Functions/Calculations - Historical Reporting - Data Capture and Transfer - Visual Discovery - Real-Time Updates - Real-Time Reporting - Data Mapping - Relational Display - Visual Analytics - OLAP - Ad hoc Query - Reporting & Statistics ## Top Databricks Alternatives - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 4.1/5.0 (131 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (714 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (356 reviews)