Databricks Data Intelligence Platform
Databricks, Inc.External reviews
768 reviews
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Fast, Reliable Lakehouse That Unifies Data Processing and SQL
What do you like best about the product?
Data processing and SQL queries are fast and reliable, and the lakehouse platform really helps unify our work.
What do you dislike about the product?
There’s a steep learning curve that demands advanced coding skills and solid Spark expertise, which can make it feel like overkill for teams that only need straightforward SQL reporting.
What problems is the product solving and how is that benefiting you?
We’ve improved our data team’s productivity by removing the need for manual infrastructure management.
Seamless, Scalable Unified Platform for Data, Analytics, and ML
What do you like best about the product?
Its unified platform for data engineering, analytics, and machine learning makes workflows seamless and scalable.
What do you dislike about the product?
It can be expensive and has a bit of a learning curve for new users.
What problems is the product solving and how is that benefiting you?
It simplifies big data processing and analytics in one platform, helping me save time and scale workflows efficiently.
All-In-One Platform That Revolutionizes Workflows
What do you like best about the product?
I love that Databricks combines data engineering, SQL analytics, and machine learning in a collaborative platform. My team and I can clean data, run SQL, and build models all in one place without losing our tasks.
What do you dislike about the product?
The price can get scary fast, and sometimes the cluster startup delay makes me feel like I am waiting for a coffee machine that's still warming up.
What problems is the product solving and how is that benefiting you?
I use Databricks to run SQL, clean data, and build models all in one place, eliminating the need for multiple tools while handling huge data efficiently.
All-in-One Collaborative Platform for Data, ML, and Analytics
What do you like best about the product?
Databricks brings together data engineering, data science, machine learning, and analytics in a single collaborative environment. You don't need to juggle multiple tools for different tasks.
What do you dislike about the product?
Databricks pricing is based on Databricks Units (DBUs), which can be expensive and difficult to estimate upfront. Costs can quickly spiral out of control, especially with auto-scaling clusters.
What problems is the product solving and how is that benefiting you?
Creating a unified lakehouse platform that combines data lakes, data warehouses, and machine learning in one place.
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.
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