Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
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Streamlined Data Processing with Unmatched Speed
What do you like best about the product?
I use Databricks for real-time data ingestion and processing as well as batch processing. I find it easy to use with PySpark, and I appreciate that it serves as a single platform for both real-time and batch processing. The in-memory processing drastically reduces processing time, and working with dataframes makes handling structured data straightforward. I like the fast execution and the ability to clean, massage, and manipulate data all on the same platform. It's also easy to deploy, and I enjoy the smooth CI pipeline with just one click. The initial setup was quite easy, and the product support made it a cakewalk.
What do you dislike about the product?
Databricks should come up with agentic framework integrated, making it a single stop for Data and AI.
What problems is the product solving and how is that benefiting you?
Databricks offers an easy-to-use platform for both realtime and batch processing. It integrates easily with PySpark and supports in-memory processing, significantly reducing processing time. Dataframes make handling structured data simpler.
Databricks Unifies Engineering and Analytics for Scalable Spark Pipelines
What do you like best about the product?
What I like best about Databricks is that it brings data engineering, processing, and analytics into one platform.
From my perspective, it makes it much easier to build and manage scalable pipelines with Spark without worrying too much about infrastructure.
From my perspective, it makes it much easier to build and manage scalable pipelines with Spark without worrying too much about infrastructure.
What do you dislike about the product?
What I dislike about Databricks is that cost control can get tricky if clusters are not managed properly.
Also, debugging distributed jobs is not always straightforward, and sometimes the UI feels a bit heavy when you just want quick insights
Also, debugging distributed jobs is not always straightforward, and sometimes the UI feels a bit heavy when you just want quick insights
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of handling large scale data processing and fragmented tools.
For me, it brings ETL, streaming, and analytics into one place, which reduces pipeline complexity and speeds up development and troubleshooting.
For me, it brings ETL, streaming, and analytics into one place, which reduces pipeline complexity and speeds up development and troubleshooting.
Powerful Lakehouse Platform with Strong Collaboration
What do you like best about the product?
Databricks is a powerful data lakehouse platform brings data engineering, AI/ML, and SQL analytics together in one collaborative workspace.
What do you dislike about the product?
The downside of Databricks is that it can be costly, especially with frequent cluster usage and poorly optimized workloads
What problems is the product solving and how is that benefiting you?
Databricks helps solve the challenge of working with large volumes of data by bring data engineering, analytics, and AI/ML into one unified platform
Streamlines Data Engineering with Ease
What do you like best about the product?
I really appreciate Databricks for its manageability. The cluster management, unified workspace, optimization, and versioning are all aspects I find incredibly valuable. The console has all the tools readily available, which is super convenient for our large scale data engineering projects. Also, the initial setup was super easy, making it a smooth transition into using the platform.
What do you dislike about the product?
norhing much
What problems is the product solving and how is that benefiting you?
I use Databricks for large scale data analysis, processing, and machine learning. It makes cluster management, workspace unification, optimization, and versioning easy with all tools handy in the console.
Databricks as a Hands On Data Engineer: Solving Real World ETL, Governance, and Lakehouse Challenges
What do you like best about the product?
I believe the most attractive thing about Databricks lies in its all-in-one nature, which makes data management easier. Previously, when I used several tools for data-related activities, the experience was not great but here everything seems to be interconnected and straightforward.
The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view.
In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required.
There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently.
The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view.
In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required.
There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently.
What do you dislike about the product?
One aspect of Databricks that i dislike is its UI. As you spend longer in using the tool, moving between notebooks and clusters becomes annoying at times.
The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects.
There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines.
At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be.
The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects.
There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines.
At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be.
What problems is the product solving and how is that benefiting you?
The most important issue that Databricks resolves is the issue of working with large volumes of data and maintaining consistency. Previously, there were separate processes for data engineering, analytics, and machine learning operations, requiring separate tools and made it difficult for me to handle but now these all are in one place, another one critical issue solved by Databricks is the issue of processing large data volumes. Utilizing the Spark, and distributed computing allows it to perform the tasks that were extremely slow on legacy systems I worked with. This has helped speed up my pipeline, although some time the delays occur.Collaboration is also another problem that Databricks addresses. Multiple users can collaborate on the same notebook or data sets. Collaboration previously was confusing, and now it is easy and good and easy and easly understandable and mainly easy sharing notebooks and assets.Scalability is another issue resolved by Databricks; there is no need to pay attention to infrastructure management. Cluster scaling depends on user requirements, saving time. Previously, it was necessary to pay more attention to the configuration of the infrastructure.
Databricks: Unified Platform for Data Processing and Analytics
What do you like best about the product?
I like that Databricks brings everything into one place, making it unnecessary to use different tools for data processing, analytics, and pipeline work. It handles large data well, and we don't have to worry about managing clusters manually. Additionally, Databricks handles collaboration and experimentation well, making it easy to try out new things.
What do you dislike about the product?
In my point of view, the one area that can be improved is cost management. If clusters aren't monitored carefully, costs can increase faster than expected. One improvement that would help is better visibility into costs at a more detailed level. More built-in alerts or recommendations when costs start increasing unexpectedly would also be helpful.
What problems is the product solving and how is that benefiting you?
Databricks helps us handle large datasets and build data pipelines. It simplifies data processing, transforming, and analysis using Spark and SQL, all in one place. It solves the problem of slow data processing spread across systems, managing infrastructure automatically and facilitating collaboration and experimentation.
Databricks Unifies Data and AI for Effortless ML at Scale
What do you like best about the product?
What I like most about Databricks is how it brings data and AI into one place, so you’re not jumping between tools.
It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking.
The integration with Apache Spark helps handle large datasets without extra setup.
Overall, it just reduces the friction between raw data and actually getting useful AI outcomes.
It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking.
The integration with Apache Spark helps handle large datasets without extra setup.
Overall, it just reduces the friction between raw data and actually getting useful AI outcomes.
What do you dislike about the product?
One thing I find challenging with Databricks is cost visibility-it can scale quickly, and predicting spend isn’t always straightforward.
There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs.
And for simpler use cases, it can feel like overkill compared to lighter-weight solutions.
There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs.
And for simpler use cases, it can feel like overkill compared to lighter-weight solutions.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of fragmented data and AI workflows by bringing everything-data engineering, analytics, and ML-into one platform.
It eliminates the need to move data across multiple systems, which reduces latency and pipeline complexity.
For me, that means faster experimentation and smoother deployment of AI models without worrying about infrastructure.
Overall, it helps focus more on solving business problems rather than managing tools.
It eliminates the need to move data across multiple systems, which reduces latency and pipeline complexity.
For me, that means faster experimentation and smoother deployment of AI models without worrying about infrastructure.
Overall, it helps focus more on solving business problems rather than managing tools.
Databricks: A Powerful Unified Platform with Room for Cost and Configuration Optimization
What do you like best about the product?
What I like best about Databricks is its ability to unify data engineering, analytics, and machine learning on a single collaborative platform.
What do you dislike about the product?
What I dislike about Databricks is that it can become expensive if clusters are not properly managed, especially when left running idle
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of managing and processing large‑scale data by unifying data engineering, analytics, and machine learning on a single platform.
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.
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