Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
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Streamlined, Collaborative Data Workflows with Powerful Performance
What do you like best about the product?
What I like most about Databricks is how it streamlines the entire data workflow by bringing processing, analysis, and machine learning into one platform. The collaborative notebook environment makes it easy to share code, context, and reasoning with teammates, which helps everyone stay aligned. It also performs strongly on large datasets while abstracting away most of the cluster management, so I can focus on solving the problem rather than dealing with infrastructure. On top of that, centralized access control and clear visibility into data usage support responsible data governance, offering a solid balance between power and ease of use.
What do you dislike about the product?
Databricks has a few downsides, although many of them feel more like trade-offs than outright negatives. My biggest concern is cost: if clusters aren’t managed carefully, expenses can climb quickly, even though the platform can scale very efficiently when it’s tuned properly. There’s also a real learning curve with Spark and distributed computing concepts, and debugging or performance tuning can be more involved than with simpler tools. Lastly, because it’s a managed service, you give up some low-level control compared with self-hosted systems, but the upside is that it takes a lot of the operational and infrastructure work off your plate.
What problems is the product solving and how is that benefiting you?
Because my client needs secure, reusable code, Databricks helps us write Python efficiently while applying OOP principles and design patterns. It also makes it straightforward to extend functionality over time and build custom code that interacts with APIs and databases.
Effortless Setup, Minimal Configuration Required
What do you like best about the product?
I use Databricks to create pipelines and data models, and I really like its minimal need for configuration. It helps me reduce the time spent on configuring accounts and processes. Databricks manages these tasks well, making my work easier. The initial setup was straightforward too, thanks to the guidance provided through the playground feature.
What do you dislike about the product?
My suggestion is to have a Genie update more as to have validations and have the table mapping in it.
What problems is the product solving and how is that benefiting you?
I find Databricks makes my work easy by minimizing the need for configuration and automating workflows, saving me time.
All-in-One Platform for Data Engineering, ML, AI, and Data Management
What do you like best about the product?
It brings all the tech stacks together in one platform—data engineering, machine learning, AI, and data management—so everything is in one place. It also includes advanced features that make the platform feel complete and capable.
What do you dislike about the product?
We need more open-source, direct connectors to both legacy and current-generation platforms to enable better data extraction. These connectors should support real-time extraction as well as real-time data rendering.
What problems is the product solving and how is that benefiting you?
It brings all types of data into one place, which makes data and access management easier. I can build data warehouses and then downstream the data to AI BI dashboards and ML models, which is very useful. Special features like the feature store, serving endpoints, AI BI dashboard, and Genie help me understand the data, work with it more effectively, and ultimately reach my goals.
All-in-One Platform That Helps Us Iterate Fast and Deploy with Confidence
What do you like best about the product?
We use Databricks daily as our core data platform for building and running pipelines across a medallion architecture, from extracting data out of SAP and Arkieva all the way to reporting-ready datasets. The notebook experience is intuitive, the feature set is massive, and Asset Bundles have made our CI/CD story with Azure DevOps really solid. Integration with cloud services was smooth, and once things are set up they just work. The learning curve can be steep for newer team members, especially around things like Unity Catalog and DABs, and costs can creep up if you're not staying on top of cluster configurations. Support is decent and the docs are strong enough that we rarely need to open a ticket. Overall, it's a powerful platform that does a lot under one roof, and it's hard to imagine our data engineering workflow without it.
What do you dislike about the product?
The cost can creep up fast if you're not careful with cluster sizing and job configurations, so it takes some effort to keep things optimized. Also, the learning curve for newer team members can be steep, especially around things like Asset Bundles, Unity Catalog, and getting the CI/CD pieces wired up properly.
What problems is the product solving and how is that benefiting you?
Databricks is solving the problem of having fragmented data spread across multiple systems like SAP and Arkieva by giving us one unified platform to extract, transform, and serve it all. That means our business teams get clean, reliable, reporting-ready data without us having to juggle a bunch of separate tools, and we can deploy and manage everything consistently across environments with confidence.
Databricks Lakehouse Powerhouse with Unity Catalog and Fast Photon SQL
What do you like best about the product?
I really value how the platform brings data lakes and warehouses together into one place. It makes managing data much easier, and the SQL performance is very fast thanks to the Photon engine. I also like the collaborative notebooks because they allow me to work with both SQL and Python seamlessly in a single environment.
What do you dislike about the product?
The cost can be high, and the DBU billing system is quite complex to track. I also found that there is a significant learning curve when it comes to Spark and configuring clusters. For smaller, quick tasks, the setup time and technical overhead can sometimes feel like a bit too much.
What problems is the product solving and how is that benefiting you?
It solves the issue of having data scattered everywhere. I love that I can switch between SQL and Python in the same spot, and the processing speed is top-notch. It’s been a game-changer for building out our financial models quickly without the usual lag.
Databricks: All-in-One Solution for Data and Analytics
What do you like best about the product?
What I like most about Databricks is that it brings everything into one place, making it easy to work on data, build models, and manage workflows. It helps teams collaborate easily in real time. It also works very fast with large data using Apache Spark, and features like automation and Delta Lake make handling big data much simpler.
What do you dislike about the product?
One thing I dislike about Databricks is that it can be expensive, especially for large workloads. Sometimes the interface and setup can feel complex for beginners. Also, managing clusters and configurations can take some effort if you’re not very familiar with it.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of handling large amounts of data efficiently.
It brings data engineering, analysis, and machine learning into one platform.
This removes the need to use multiple tools.
It helps in faster data processing using Apache Spark.
It makes collaboration easier for teams.
It simplifies building and managing data pipelines.
It improves data reliability with features like Delta Lake.
It reduces manual work through automation.
It saves time and effort in daily tasks.
Overall, it helps me work faster and more efficiently with data.
It brings data engineering, analysis, and machine learning into one platform.
This removes the need to use multiple tools.
It helps in faster data processing using Apache Spark.
It makes collaboration easier for teams.
It simplifies building and managing data pipelines.
It improves data reliability with features like Delta Lake.
It reduces manual work through automation.
It saves time and effort in daily tasks.
Overall, it helps me work faster and more efficiently with data.
Empowers Collaborative Data Science with Minor Learning Curve
What do you like best about the product?
I use Databricks for a lot of things. The main ones are making sense out of the data, looking at chunks of data, and doing machine learning. Databricks makes these tasks very easy and helpful, especially for data projects. It's great for collaborating with friends and developing my Python code in notebooks. I like Databricks because it has good capabilities for handling big data and is excellent for working with the data and machine learning. It's also easy to use when working with people, as many can work on a project and share their findings.
What do you dislike about the product?
Databricks is very powerful, but there are some things that need improvement. It's hard to learn for beginners when working with Spark and setting up clusters, as this was confusing at first. Sometimes the interface and settings can feel complicated. I think it would be helpful if there were clear setup instructions so new users could get started easily with Databricks.
What problems is the product solving and how is that benefiting you?
I use Databricks to make sense of data, collaborate with others, and develop Python code. It simplifies data engineering, machine learning, and handling data while allowing multiple people to work on notebooks simultaneously.
Efficient Data Processing with Robust Governance
What do you like best about the product?
I use Databricks for data engineering tasks like cleaning, analysis, and performing ETL from source environments to the cloud. I find it very good for governance, and it's easy to process the data. The catalog feature is particularly useful for governance, making it easier to manage data efficiently. Databricks offers very fast processing and efficient governance capabilities, which is why my team switched from ADF to Databricks. Additionally, the initial setup was very easy to understand.
What do you dislike about the product?
Reporting stuffs needs to improve
What problems is the product solving and how is that benefiting you?
Databricks makes governance straightforward and simplifies data processing for our projects. Its catalog is essential for data governance. We switched from ADF to Databricks for its fast processing and efficiency in governance.
Databricks: Intuitive, Unified Platform with Seamless Integrations and Fast Support
What do you like best about the product?
As a data engineer, Databricks has become my go-to platform for end-to-end data work. The ease of use is outstanding notebooks, Delta Live Tables, and Genie all have intuitive interfaces that reduce rampup time significantly. Implementation was smooth thanks to excellent documentation and responsive customer support that actually resolves issues fast. I use it daily, and the sheer number of features from Unity Catalog to AI/BI Genie keeps growing. Integration with cloud storage, BI tools, and ML frameworks is seamless, making it a true unified platform.
What do you dislike about the product?
One challenge is the lack of cost transparency at a granular job level it's difficult to pinpoint exactly which pipeline or notebook is driving up DBU consumption without investing in custom monitoring. Auto scaling clusters, while powerful, can silently balloon costs overnight if not carefully configured with proper limits. Additionally, the SQL warehouse tiers can be confusing to choose from upfront, making budget planning tricky for teams. A built in cost allocation dashboard per job or user would be a huge improvement for day to day cost governance.
What problems is the product solving and how is that benefiting you?
Databricks has eliminated the silos between our data engineering, analytics, and ML teams. Previously, we juggled multiple tools for ingestion, transformation, and reporting. Now everything lives in one lakehouse. Genie specifically has been a game-changer business stakeholders can ask natural language questions directly against our data without writing SQL, which dramatically reduces ad-hoc request bottlenecks for our engineering team. Decision making is faster, data is more democratized, and we've cut our reporting pipeline overhead by a significant margin.
A Reliable Workhorse for Data Engineering and Analytics
What do you like best about the product?
The unified platform approach is what I appreciate most. Having notebooks, data engineering pipelines, ML workflows, and SQL analytics all in one place saves a ton of time instead of juggling multiple tools. The collaborative notebooks make it easy to share work with teammates, and the cluster management has gotten a lot smoother over time. Delta Lake integration is also a huge plus for keeping our data reliable and consistent.
What do you dislike about the product?
The cost can get out of hand pretty quickly if you're not careful with cluster sizing and uptime. It's not always obvious how to optimize spending, and the pricing model feels complex. The learning curve for new team members is also steeper than I'd like, especially for people who aren't already familiar with Spark. Sometimes the UI can feel sluggish when working with larger notebooks, and debugging job failures could be more straightforward.
What problems is the product solving and how is that benefiting you?
Databricks helps me resolve complex ETL pipeline failures and persistent data quality issues in supply chain analytics by unifying batch and streaming processing from SAP systems with Delta Live Tables. It also removes a lot of the infrastructure management headaches thanks to auto-scaling clusters, so I can stay focused on writing code for multi-terabyte workloads instead of constantly worrying about cluster sizing.
For my manufacturing data projects, Databricks accelerates development cycles from weeks to days via collaborative notebooks and DLT pipelines, enabling faster Power BI reporting and stakeholder decisions. Unity Catalog centralizes governance across Azure and SAP sources, preventing schema drift that plagued prior Hive-based lakes.
For my manufacturing data projects, Databricks accelerates development cycles from weeks to days via collaborative notebooks and DLT pipelines, enabling faster Power BI reporting and stakeholder decisions. Unity Catalog centralizes governance across Azure and SAP sources, preventing schema drift that plagued prior Hive-based lakes.
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