--- title: Azure Databricks Reviews meta\_title: 'Azure Databricks Reviews 2026: Details, Pricing, & Features | G2' meta\_description: Filter 239 reviews by the users' company size, role or industry to find out how Azure Databricks works for a business like yours. aggregate\_rating: rating\_value: 4.5 review\_count: 239 scale: '5' date\_modified: '2026-08-11' parent\_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Azure Databricks Reviews & Product Details Claimed ###### Profile Status This profile is currently managed by Azure Databricks but has limited features. Are you part of the Azure Databricks team? [Upgrade your plan](https://sell.g2.com) to enhance your branding and engage with visitors to your profile! Azure Databricks is a unified, open analytics platform developed collaboratively by Microsoft and Databricks. Built on the lakehouse architecture, it seamlessly integrates data engineering, data science, and machine learning within the Azure ecosystem. This platform simplifies the development and deployment of data-driven applications by providing a collaborative workspace that supports multiple programming languages, including SQL, Python, R, and Scala. By leveraging Azure Databricks, organizations can efficiently process large-scale data, perform advanced analytics, and build AI solutions, all while benefiting from the scalability and security of Azure. Key Features and Functionality: - Lakehouse Architecture: Combines the best elements of data lakes and data warehouses, enabling unified data storage and analytics. - Collaborative Notebooks: Interactive workspaces that support multiple languages, facilitating teamwork among data engineers, data scientists, and analysts. - Optimized Apache Spark Engine: Enhances performance for big data processing tasks, ensuring faster and more reliable analytics. - Delta Lake Integration: Provides ACID transactions and scalable metadata handling, improving data reliability and consistency. - Seamless Azure Integration: Offers native connectivity to Azure services like Power BI, Azure Data Lake Storage, and Azure Synapse Analytics, streamlining data workflows. - Advanced Machine Learning Support: Includes pre-configured environments for machine learning and AI development, with support for popular frameworks and libraries. Primary Value and Solutions Provided: Azure Databricks addresses the challenges of managing and analyzing vast amounts of data by offering a scalable and collaborative platform that unifies data engineering, data science, and machine learning. It simplifies complex data workflows, accelerates time-to-insight, and enables the development of AI-driven solutions. By integrating seamlessly with Azure services, it ensures secure and efficient data processing, helping organizations make data-driven decisions and innovate rapidly. * * * Seller [Microsoft](https://www.g2.com/sellers/microsoft) Discussions [Azure Databricks Community](https://www.g2.com/products/azure-databricks/discuss) Solution Type All-in-One Overview by Avinash Kumar (Senior Software Engineer(Data Science) at Mindtree) Show More ## Value at a Glance Averages based on real user reviews. ### Time to Implement 3 months ### Return on Investment 23 months [ View More Pricing Information ](https://www.g2.com/products/azure-databricks/pricing) ## Top-Rated Alternatives [ ![Alteryx](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Alteryx") Alteryx 4.6/5(893) ](https://www.g2.com/products/alteryx/reviews) [ ![Snowflake](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Snowflake") Snowflake 4.6/5(764) ](https://www.g2.com/products/snowflake/reviews) [ ![Splunk Enterprise](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Splunk Enterprise") Splunk Enterprise 4.3/5(434) ](https://www.g2.com/products/splunk-enterprise/reviews) [ View All Alternatives ](https://www.g2.com/products/azure-databricks/competitors/alternatives) ## User Insights Average based on 239 real user reviews. Implementation Time 3 months Perceived Cost $$$$$ [Log in to unlock pricing and user insights](/login) ## Azure Databricks Integrations (5) What do users say about integrations? Integration information sourced from real user reviews. [ ![Product Avatar Image](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Product Avatar Image") Azure Data Factory ](https://www.g2.com/products/azure-data-factory/reviews)[ ![Product Avatar Image](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Product Avatar Image") Azure Data Lake Store ](https://www.g2.com/products/azure-data-lake-store/reviews)[ ![Product Avatar Image](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Product Avatar Image") Azure Synapse Analytics ](https://www.g2.com/products/azure-synapse-analytics/reviews)[ ![Product Avatar Image](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Product Avatar Image") Microsoft Power BI ](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews)[ ![Product Avatar Image](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Product Avatar Image") Snowflake ](https://www.g2.com/products/snowflake/reviews) Show More ![Wealth A.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Wealth A.") WA Wealth A. Business Intelligence Analyst/ Designer Financial Services Enterprise (\> 1000 emp.) 4/24/2026 "Azure Databricks efficient for large data, a bit rough on edges" 4.5/5 What do you like best about Azure Databricks? What I like most about Azure Databricks is how it makes working with data feel straightforward without me having to overthink the setup. From my experience, I mostly use it for querying, transforming, and validating data, and it handles large datasets really well without slowing me down. I don’t have to worry too much about performance — I just write what I need, and it runs. I also like the flexibility of switching between SQL and PySpark depending on what I’m doing. It makes it easier to explore data and troubleshoot issues quickly without being stuck in one approach. The notebook environment is another big plus for me. I use it to organize my queries and logic in one place, so I can always go back, adjust things, or reuse parts without starting from scratch. Overall, it just makes my workflow cleaner and more efficient, especially when I’m working with large volumes of data and need quick, reliable results. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? What I dislike about Azure Databricks, based on how I’ve used it, is mostly tied to day-to-day usability. When I’m working with files (especially around /dbfs), I sometimes run into seemingly random errors that aren’t very clear. It takes extra time to figure out what actually went wrong, which is frustrating when I’m just trying to get quick results. Debugging is another area that can slow me down. If a query or transformation doesn’t behave as expected, it isn’t always obvious where the issue is, so I end up spending more time tracing and narrowing things down than I’d like. The notebook environment is useful, but as a single notebook grows, it can get messy and harder to manage. If I’m not careful, it’s easy to lose structure and organization. Cost is also something I’ve had to keep an eye on. Even when I’m only testing or running queries, usage can add up quickly if resources aren’t managed properly. Overall, it works well, but there are still moments where it feels less intuitive than it should—especially when something goes wrong. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? Azure Databricks mainly helps me work with large, scattered datasets in a way that’s actually manageable. In my experience, before using it, handling data across different sources or tools could get messy—especially when I needed to query, clean, and validate everything in a consistent way. With Databricks, I can do all of that in one place, which makes my process much simpler. It also takes away a lot of the stress around performance. I don’t have to worry as much about how my queries will scale as datasets grow—I can focus on writing what I need, and it handles the rest. That’s been especially helpful when I’m exploring or validating large volumes of data. Speed is another big plus. I can run queries quickly, test transformations, and iterate without long waits, which keeps my workflow moving and makes me more efficient. Overall, it makes my data work more straightforward and less fragmented. I spend less time jumping between tools or dealing with performance issues, and more time actually understanding and working with the data. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerSource: Organic Review from User Profile ![Ravi V.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Ravi V.") RV Ravi V. Data Engineer Mid-Market (51-1000 emp.) 4/8/2026 "Balancing Performance and Complexity in Azure Databricks" 5/5 What do you like best about Azure Databricks? I love Azure Databricks most for its seamless collaborative notebooks—teams can code, visualize, and iterate together in real-time without hassle. The autoscaling Spark clusters handle massive data effortlessly, so no more babysitting resources. Integration with Azure services like Data Lake feels native and smooth. Delta Lake keeps everything reliable and versioned. Plus, built-in ML tools make model building a breeze. It's like a powerhouse that just works for data pros. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? The primary downsides of Azure Databricks are its high and often unpredictable costs, as the combination of DBU units and underlying Azure VM fees can escalate quickly without strict governance. Additionally, the long cluster cold-start times can be frustrating for developers used to the instant responsiveness of serverless environments, often leading to wasted time or expensive "always-on" configurations. Finally, the steep learning curve required to manage Spark optimizations and complex security integrations can feel overkill for smaller teams that just need simple data processing. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? Azure Databricks eliminates the complexity of managing fragmented data silos by providing a unified Lakehouse platform for engineering, AI, and analytics. It benefits me by automating infrastructure scaling and offering collaborative notebooks, which significantly accelerates the transition from raw data to production-ready insights. By integrating seamlessly with the Azure ecosystem, it reduces operational overhead and allows me to focus entirely on building high-value data solutions. Review collected by and hosted on G2.com. Show More 4/10/2026 Current UserValidated ReviewerIncentivizedSource: G2 invite ![Lokesh S.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Lokesh S.") LS Lokesh S. Senior Data Scientist Mid-Market (51-1000 emp.) 6/18/2026 "A powerhouse for scaling ML workflows, but keep a close eye on your billing." 5/5 What do you like best about Azure Databricks? I'm a Senior Data Scientist at a mid-sized company where I work a lot with Azure Databricks to connect data in our production machine learning models to the raw data lakes. My team uses it every day to clean huge amounts of user behaviour data, build complex features and develop predictive models, our Customer Churn risk and Product recommendation models.I love how black and white it's (almost) the headache of big data infrastructure is eliminated. Prior to adopting Databricks, creating and maintaining a Spark cluster had been a tedious project, which necessitated significant data engineering support. Now I simply choose my compute size from a dropdown and head straight to writing code in PySpark, Python or SQL in a collaborative notebook. My team's dream has also come true with the native integration of MLflow. Previously, recorded model version, parameters and metrics was stored in handwritten spreadsheet files and pickle files. This is now the case for every experiment, and automatic logging puts comparisons of model runs or reverting back to an earlier version a breeze. It's also cool that it integrates with our Azure Data Lake storage without having to go through security hurdles to mount data onto it. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? The worst thing is that the bill can add up quickly if you're not managing your compute usage. Auto-scaling is a godsend for performance but we do have some serious billing shock-and-tumbles when clusters were scaling up feverishly with some badly written query left running, that could have been avoided. Another one of the frustrations is the StartUp Time for Clusters. The minutes required to spin up a cluster just to run an ad-hoc data check, and then wait for the results can really bring you out of the flow. Lastly, the Spark UI can be a bit confusing and unwelcoming for newer, less technical members of the team who are simply looking to run some simple SQL queries, instead of getting into spark configuration or compute policies. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? But the way Databricks has revolutionized managing large-scale data processing and model deployment is truly groundbreaking. One example we can take is our batch scoring program for recommending customers, which happens every week. The original implementation ran on a single large virtual machine and was not only very time consuming (nearing 8 hrs to run), but was also prone to crashing in the middle because of memory restrictions. We moved that pipeline to an Azure Databricks notebook, and then distributed the processing power using PySpark without any crashes, and the time to run it dropped under 45 minutes. It's also helped a lot with our collaboration wall in the works. Our data engineers and data scientists can collaborate in the same data environment on the exact same dataframes without having to wait between each other; the time to have a predictive model in a local prototype running in the real business environment is significantly reduced. Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: G2 invite ![Tej P.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Tej P.") TP Tej P. DevOps Engineer Enterprise (\> 1000 emp.) 3/20/2026 "Comprehensive Data Management and Streamlined Setup" 5/5 What do you like best about Azure Databricks? I use Azure Databricks to build and manage data pipelines. It provides all required services in a single place, like data engineering, SQL, and ML features. It helps me simply process large-scale data for enterprise projects, making Azure Databricks a valuable tool for me. The SQL features make it easy to query and analyze data quickly, and the ML capabilities support experimenting with models on the same platform. The initial setup is very easy; you just need to create a resource on the Azure portal by entering the resource group and Databricks workspace name with the rest of the default settings. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? Cost optimization: it can be more optimized by providing the single cost monitoring dashboard by default for the workspace admins, as they have this budget feature in the preview for the account console only. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? I use Azure Databricks to build and manage data pipelines, simplifying the processing of large-scale enterprise data. It lets me create scalable ETL pipelines, quickly query data with SQL, and experiment with ML models using Mosaic AI on the same platform. Review collected by and hosted on G2.com. Show More 3/24/2026 Current UserValidated ReviewerIncentivizedSource: G2 invite ![Mayuri K.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Mayuri K.") MK Mayuri K. Product Management Fellow Mid-Market (51-1000 emp.) 5/1/2026 "All-in-One Data Platform with an Intuitive, User-Friendly Interface" 5/5 What do you like best about Azure Databricks? It makes data easy and simple to understand even if we are not from technical background, i dont need to swap or switch different app or software now for data eng , analytics or data science all can be done in once now. The interface is very good and user friendly easy to understand tabs given , i have tried uploding a large set of data , uploading experience was very smooth and easy Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? Mostly, I got confused during the cluster setup. It was very difficult for me, and even with the settings I’m still struggling with it. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? For me its helping mostly in getting faster insights, in tracking the perfomnce of the task assigned and outcomes on it Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: G2 invite ![Elisa L.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Elisa L.") EL Elisa L. Consultant Data&AI Mid-Market (51-1000 emp.) 4/30/2026 "Azure Databricks: Scalable, Fast Collaboration with Seamless Azure Integration" 4.5/5 What do you like best about Azure Databricks? What I like best about Azure Databricks is how well it combines scalability, speed, and collaboration in a single environment. It makes it easy to work with large datasets, build and run data pipelines efficiently, and support both engineering and analytics tasks without switching between too many tools. I also appreciate how smoothly it integrates with the broader Azure ecosystem, which makes it especially useful for end-to-end data processing and analytics workflows Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? One thing I dislike about Azure Databricks is that it can feel complex and not always immediately intuitive, especially at the beginning. The environment is powerful, but that also means there are many concepts, configurations, and moving parts to get used to before it feels really smooth. Another drawback is that, for some tasks, the setup and navigation can feel heavier than expected, which slows down simple workflows. In short, it is a very capable platform, but the learning curve and operational complexity can make it less straightforward than I would like. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? Azure Databricks addresses the hassle of juggling separate tools for engineering, analytics, and AI by bringing everything into a single platform. That consolidation reduces friction and helps speed up delivery. For me, it means I can work more efficiently with large datasets, build pipelines, and collaborate in the same environment without constantly switching contexts. It also helps that the platform is built for scalable processing and integrated workflows, so the path from exploration to production feels much smoother and more consistent. Review collected by and hosted on G2.com. Show More Validated ReviewerSource: Organic SA Suraj A. Data Enigneer Mid-Market (51-1000 emp.) 2/4/2026 "Azure Databricks: Unified, Scalable Data Platform That Boosts Productivity" 5/5 What do you like best about Azure Databricks? What I like best about Azure Databricks is how it simplifies large-scale data processing while still giving flexibility to engineers. From my experience, the biggest advantage is the unified platform I can do data engineering, transformations, performance tuning, and even analytics in one place without jumping across multiple tools. The integration with Spark is seamless, and things like auto-scaling clusters, job scheduling, and notebook collaboration make day-to-day work much more efficient. I also appreciate features like Delta Lake handling ACID transactions, schema evolution, and time travel directly on data lakes makes production pipelines much more reliable. On top of that, optimizations like Adaptive Query Execution, auto-optimize, Z-ordering, and caching really help when working with large datasets. Another thing I like is how well it integrates with the Azure ecosystem whether it’s ADLS, ADF, Key Vault, or Unity Catalog for governance. It reduces a lot of setup overhead and makes deployments smoother across environments. Overall, it lets me focus more on solving data problems and performance tuning rather than worrying about infrastructure management. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? One thing I dislike about Azure Databricks is that cost management can get tricky if clusters and jobs aren’t monitored closely. Because it’s so easy to spin up clusters and run large workloads, costs can increase quickly especially with auto-scaling or multiple parallel jobs running. So it requires good governance and monitoring in place. Another area is debugging and troubleshooting. While notebooks are great for development, debugging production job failures especially intermittent Spark or infrastructure issues can sometimes take time. Logs are available, but tracing the exact root cause across cluster events, Spark UI, and job runs isn’t always straightforward. I’ve also noticed that handling CI/CD and deployments (like moving notebooks, workflows, configs across environments) isn’t as smooth out of the box compared to traditional code repos. It’s improving with Databricks Asset Bundles and Repos, but still needs careful setup. That said, most of these are manageable with best practices cost controls, monitoring, and proper DevOps processes. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? Azure Databricks is mainly solving the problem of processing and managing large-scale data efficiently in a unified environment. Before platforms like Databricks, handling big data required setting up separate tools for storage, compute, scheduling, and processing. It involved a lot of infrastructure management and integration effort. Databricks brings all of this together scalable Spark compute, collaborative notebooks, job orchestration, and optimized storage layers in one place. From a data engineering perspective, it solves challenges like processing huge volumes of data, handling complex transformations, and building reliable pipelines. Features like Delta Lake help address data consistency and reliability issues for example, ACID transactions, schema enforcement, and time travel make production data pipelines safer and easier to manage. It also solves performance problems. Optimizations like Adaptive Query Execution, caching, auto-scaling clusters, and partition pruning help process data faster without heavy manual tuning. How it benefits me personally: For me, it reduces the time spent on infrastructure setup and lets me focus more on data logic and optimization. I can quickly develop pipelines, test transformations in notebooks, and deploy jobs to production with better monitoring. It also improves productivity collaboration through shared notebooks, integration with Azure services like ADLS and ADF, and centralized governance through Unity Catalog make day-to-day work smoother. Overall, it helps me build scalable, reliable, and high-performing data solutions faster than traditional big data setups. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerSource: Organic ![NOOR A.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "NOOR A.") NA NOOR A. Data Engineer Information Technology and Services Enterprise (\> 1000 emp.) 10/18/2025 "A Powerful and Reliable Platform for Scalable Data Engineering" 5/5 What do you like best about Azure Databricks? What I like best about Azure Databricks is how seamlessly it integrates with the Azure ecosystem — especially with services like Data Lake, Synapse, and Data Factory. It provides an excellent balance between ease of use and advanced capabilities, allowing both technical and non-technical users to collaborate in a single environment. The notebooks are intuitive and support multiple languages such as SQL, Python, and R, which makes implementation and experimentation smooth. I use it frequently for building and managing data pipelines, running transformations, and developing machine learning models. The platform’s scalability, auto-scaling clusters, and managed Delta Lake features make handling large datasets efficient. Customer support is generally helpful and the platform continues to evolve with frequent updates that add even more useful features. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? Although Azure Databricks is powerful, a few areas could be improved. The initial setup and environment configuration can be slightly complex for new users, and cluster startup times can sometimes be slow. The pricing structure also requires careful monitoring — costs can increase quickly if clusters aren’t optimized or auto-terminated properly. While the interface is robust, it could be more beginner-friendly, and notebook version control could be smoother. Customer support response time can vary depending on the issue severity. Still, once you get accustomed to the environment, it’s a highly capable and dependable platform for daily data workloads and analytics. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? Azure Databricks has addressed several major data challenges within our organization. Previously, handling large datasets, integrating various data sources, and executing complex transformations were both time-consuming and prone to errors. With Databricks, I am able to develop scalable ETL pipelines and automate data workflows more efficiently, which has greatly reduced manual work and shortened processing times. The platform also offers a collaborative environment where data engineers and analysts can work together smoothly, enhancing productivity and minimizing miscommunication. Its integration with Azure services such as Data Lake, Data Factory, and Synapse ensures seamless data movement throughout our ecosystem. This has enabled us to deliver reliable, high-quality datasets more quickly for reporting, analytics, and machine learning projects, ultimately supporting better business decisions and greater operational efficiency. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerSource: Organic ![Akshat G.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Akshat G.") AG Akshat G. Programmer Analyst Information Technology and Services Small-Business (50 or fewer emp.) 12/3/2025 "Effortless Data Processing and Seamless Azure Integration" 4.5/5 What do you like best about Azure Databricks? The platform manages large-scale data processing with impressive smoothness, and its interface becomes quite user-friendly after a short learning curve. Integrating it with other Azure services is straightforward, which significantly speeds up the implementation process. I appreciate the variety of features available for ETL and analytics, allowing us to use it regularly for a range of different workloads. When problems arise, the documentation and support resources are generally sufficient to help resolve issues quickly. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? Sometimes, the platform can seem a little complicated for newcomers, and it may take some time for clusters to start up. Managing costs is not always straightforward, and certain features require additional configuration. While support is generally helpful, response times can occasionally be slow. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? This tool enables us to process large datasets efficiently and construct dependable ETL pipelines. By consolidating data cleaning, transformation, and analytics into a single collaborative platform, it streamlines our workflow. The integration with Azure storage and other services is a significant time-saver, and the increased processing speed has a direct positive impact on our reporting and decision-making. Review collected by and hosted on G2.com. Show More Validated ReviewerSource: Organic ![Muzammil A.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Muzammil A.") MA Muzammil A. IT Technician, IT Infrastructure Operations Mid-Market (51-1000 emp.) 3/30/2026 "Efficient, Scalable Data Processing Powerhouse" 4.5/5 What do you like best about Azure Databricks? I use Azure Databricks for data processing, ETL, and analytics on large datasets. I like its scalability and easy collaboration in one unified platform. I appreciate its fast performance, seamless integration with other Azure services, and user-friendly notebooks. The initial setup was very easy, especially with the guidelines provided on the website. Review collected by and hosted on G2.com. What do you dislike about Azure Databricks? Cost management, fast cluster startup times, and a more intuitive UI for beginners. Review collected by and hosted on G2.com. What problems is Azure Databricks solving and how is that benefiting you? I use Azure Databricks to efficiently process large datasets, simplify ETL workflows, and enable fast, scalable data analysis on one platform. Review collected by and hosted on G2.com. Show More Validated ReviewerSource: Organic ## Questions about Azure Databricks? Ask real users or explore answers from the community Get practical answers, real workflows, and honest pros and cons from the G2 community or share your insights. [ Ask about Azure Databricks ](https://www.g2.com/products/azure-databricks/discussions/new) ![Aman N.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Aman N.") AN Aman Nigam • Last activity about 6 years ago What is the best way to databricks in ADF 1 Upvote 1 [ Join the conversation ](https://www.g2.com/discussions/26515-what-is-the-best-way-to-databricks-in-adf) ![Avinash K.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Avinash K.") AK Avinash Kumar • Last activity over 2 years ago When data is small how can I reconfigure cluster to automatically adjust . I don't know which day data coming will be small. 1 Upvote 1 [ Join the conversation ](https://www.g2.com/discussions/azure-databricks-when-data-is-small-how-can-i-reconfigure-cluster-to-automatically-adjust-i-don-t-know-which-day-dat) [ View all Discussions ](https://www.g2.com/products/azure-databricks/discuss) ## Pricing Insights Averages based on real user reviews. ### Time to Implement 3 months ### Return on Investment 23 months ### Perceived Cost $$$$$ [ View More Pricing Information ](https://www.g2.com/products/azure-databricks/pricing) Azure Databricks Comparisons ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/small_square/small_square_791b528c516cc1b08151fa6da3988161/dataiku.png "Product Avatar Image") Dataiku 4.4/5(224) [ Compare Now ](https://www.g2.com/compare/azure-databricks-vs-dataiku) ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/small_square/small_square_f176b4154a751d10150daa67a57b7dc5/azure-data-lake-analytics.jpg "Product Avatar Image") Azure Data Lake Analytics 4.2/5(37) [ Compare Now ](https://www.g2.com/compare/azure-data-lake-analytics-vs-azure-databricks) ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/small_square/small_square_5015a6ccbbe12278ba4392c1c0d806be/ibm-cloud-pak-for-data.png "Product Avatar Image") IBM Cloud Pak for Data 4.3/5(90) [ Compare Now ](https://www.g2.com/compare/azure-databricks-vs-ibm-cloud-pak-for-data) ##### ##### Azure Databricks Features Data Transformation Real-Time Analytics Data Querying Connectivity Hadoop Integration Spark Integration Multi-Source Analysis Operations Data Visualization Data Workflow Governed Discovery [ View More Features ](https://www.g2.com/products/azure-databricks/features) ##### Categories on G2 [Big Data Analytics](https://www.g2.com/categories/big-data-analytics) ##### Explore More [Employee communications platforms](https://www.g2.com/discussions/employee-communications-platforms-need-help-choosing-a-solution-advice-needed)[Best software for integrating big data with analytics platforms](https://www.g2.com/discussions/what-is-the-best-software-for-integrating-big-data-with-analytics-platforms)[Best software for compliance-friendly absence reporting](https://www.g2.com/discussions/best-software-for-compliance-friendly-absence-reporting) [What are the best event management services platforms for coordinating hundreds of events across multiple venues?](https://www.g2.com/discussions/what-are-the-best-event-management-services-platforms-for-coordinating-hundreds-of-events-across-multiple-venues%20)[Which retail media platforms are worth using for a CPG company that wants to integrate retail media spend data with its broader marketing mix model without a custom data pipeline?](https://www.g2.com/discussions/which-retail-media-platforms-are-worth-using-for-a-cpg-company-that-wants-to-integrate-retail-media-spend-data-with-its-broader-marketing-mix-model-without-a-custom-data-pipeline)[Pros and Cons Details](https://www.g2.com/products/azure-databricks/reviews?qs=pros-and-cons) [Show MoreShow Less](javascript:void(0);)