Databricks Data Intelligence Platform Reviews & Product Details

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

The single word I can say about Data bricks is they have really top class services in the field of data management. Their platform have power to handle huge data with very simple modules without any extra integrations. The services we are using from Data bricks gives freedom to handle data on every single steps. Review collected by and hosted on G2.com.

Whenever we need to store data and analyze the data we use Data bricks analytical views for that and their really gives best insights from the data. Review collected by and hosted on G2.com.

its seamless integration of data engineering, data science, and machine learning workflows in one unified platform. It enhances collaboration, accelerates data processing, and provides scalable solutions for complex analytics, all while maintaining a user-friendly interface. Review collected by and hosted on G2.com.

Another drawback is the limitation with RDDs and the lack of support for ML Runtime in the standard compute tier.

The pricing model, which can become expensive as usage scales, especially for smaller teams or projects. Review collected by and hosted on G2.com.

The advanced features of Data bricks gives very effective solution for data analytics and data management. They have all features that we really need to manage the huge data. Quality of their services and easy integration make the my favorite platform for data management. Review collected by and hosted on G2.com.

I really apricate their support team that always be their and provide quick reply on any query regarding our tool and integration. I never felt any bad experience yet won their platform. Review collected by and hosted on G2.com.

The reliability of Data bricks always gives a value added services to store huge data with full security. Their integration and number of features provide easy implementation that manage our all data pipeline with best analytical dashboard. Review collected by and hosted on G2.com.

All the services we are getting from data bricks is top quality and we don't have any comment o their dislike yet. We are really appreciate their quality services in our organization. Review collected by and hosted on G2.com.

As a data engineer who has been working with Databricks for the past two years, I can honestly say the platform has completely transformed the way we approach data engineering projects. Before Databricks, me and my team often faced challenges with managing large datasets and ensuring smooth collaboration between data engineers and data scientists. There were times when workflows felt disjointed, and troubleshooting issues across different tools consumed a lot of our time.

Databricks has changed all of that. The collaborative notebooks feature, in particular, has been a game-changer. I can now work seamlessly with data scientists in real-time, troubleshooting issues and iterating on solutions much faster. For example, during a recent project, we were able to refine a machine learning model within days, thanks to the ability to easily share notebooks and quickly run experiments together. This level of collaboration used to take weeks with previous tools.

The auto-scaling feature has been a lifesaver. I vividly remember struggling with performance issues when processing large datasets on our old infrastructure. Now, Databricks automatically adjusts resources based on workload, so we never have to worry about managing compute power. This has drastically cut down on processing times. For instance, a data transformation job that used to take hours now finishes in a fraction of the time, allowing us to deliver projects faster.

Delta Lake has also been invaluable. Before we started using it, data consistency and quality were constant concerns, especially when dealing with large and varied data sources. Now, with Delta Lake, we can trust that our data is not only high quality but also easily accessible and queryable. One particular example was when we had to rebuild a complex dataset pipeline. Delta Lake allowed us to work with incremental data updates, making the process much more efficient and reliable.

In short, Databricks has greatly reduced development time and improved the overall quality of our deliveries. It’s helped me streamline complex workflows, improve collaboration across teams, and most importantly, deliver data-driven solutions faster and with greater confidence. Review collected by and hosted on G2.com.

Cost Optimisation - While I appreciate the granular billing information provided, predicting costs for large projects or shared environments can still feel opaque. Many teams struggle to control runaway costs from idle clusters or suboptimal configurations. Introducing smarter autoscaling and recommendations tailored to our workloads would be invaluable. For instance, alerts for "idle clusters" or "cost hotspots" in our environment could proactively save budgets and improve efficiency.

Simplified Governance and Security - Managing access at fine-grained levels can be cumbersome. For example, controlling who can view versus who can execute a notebook or job often requires workarounds. Audit logs are excellent, but making sense of them for actionable insights sometimes feels like solving a puzzle. Enhanced attribute-based access control (ABAC) and more intuitive UI-based controls for permission management would greatly streamline operations.

User Experience - The collaborative notebook interface is one of Databricks' standout features, yet there are areas where it could be smoother. Collaboration is sometimes hindered when two users edit the same notebook. Version control feels basic compared to Git-based systems. Debugging within notebooks, especially for non-Python workloads, could use significant improvement. Adding inline commenting, conflict resolution tools, and robust debugging features would take the platform to the next level. A workspace-level activity feed to show what’s happening in shared projects would also be immensely helpful.

Workflow Automation - Include AI-driven insights for optimizing workflows (e.g., spotting bottlenecks or inefficiencies). Enable easier integration with external workflow automation tools. Review collected by and hosted on G2.com.

The experience of data visualization with data bricks is very great and their top notch services make work more easier and smooth for machine learning. They have integration where we can easily deploy ML modules on them without any extra integration. Review collected by and hosted on G2.com.

All are good and very supportive, their team always be their whenever we need any help they replied very quick with resolution. i really enjoyed their multi features integration that make them our frequent tool for ML and Dep learning devloping. Review collected by and hosted on G2.com.

The best part of Data bricks is they provide multiple features to us that manage all data management work at one place without any issues and also they have very effective data intelligence tools. Review collected by and hosted on G2.com.

Easy to use of their implementation gives best support in our work that gives next level of experience and best user experience to work on their platform. Review collected by and hosted on G2.com.