Databricks Data Intelligence Platform
Databricks, Inc.External reviews
768 reviews
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External reviews are not included in the AWS star rating for the product.
Robust Data Processing with High Costs and Learning Curve
What do you like best about the product?
What I like the most about Databricks is the performance. When handling a large amount of datasets, it's time-consuming and requires a lot of effort to clean and use the data, but Databricks allows me to do all of that on a single platform. I appreciate that I can have all the tools for processing, storage, and analysis in one place. The notebook feature is also very good, providing a nice experience for writing code, creating pipelines, and more.
What do you dislike about the product?
Things I want to be improved in Databricks is the cost. Like, it's really expensive to be used when we are small scale businesses. I would like the cost to be decreased. There's also a lot of configuration and setup, which makes it hard for junior level users to just start and work with it. It needs more learning modules to help start with the platform. Initial setup wasn't easy, needing certification on data exposure and it was tough because engineers couldn't reset the database properly.
What problems is the product solving and how is that benefiting you?
I use Databricks for large-scale data and analytics, making it easier to handle and process large datasets. It helps analyze data faster, enables efficient data cleaning and transformation, and provides all tools in a single platform for processing, storage, and analysis.
Unified Analytics Powerhouse with Minor Hiccups
What do you like best about the product?
I like most about Databricks is that it brings data engineering, analytics, and AI workflow into one shared platform, which makes collaboration much easier. It's valuable for working with a large dataset and notebooks, and it helps set up suitable pipelines without the hassle of managing too many separate tools.
What do you dislike about the product?
Cost visibility and resource users can be hard to track, especially as more teams cluster and jobs start using the platform. I also like to sync up permission management. Clear troubleshooting for a job failure and a smoother experience around the workspace governance and configuration. CDC lake flow is always stuck for a last table and not giving a clear picture till now. Serverless logs are sometimes very difficult to track, making it hard to understand the reason for job failures.
What problems is the product solving and how is that benefiting you?
I find Databricks solves handling large-scale data processing and analytics by unifying data engineering, analytics, and AI workflow into one platform. It simplifies collaboration on notebooks and automation workflows, enabling faster work with big datasets using Spark.
BI and Data Engineering in One Place, with AI-Assistant
What do you like best about the product?
Possibility to combine data warehousing and data lakes into a “lakehouse.” So I can do BI and data engineering all in one place instead of stitching together multiple systems.
Using AI to improve and make faster the SQL writing and execution
Using AI to improve and make faster the SQL writing and execution
What do you dislike about the product?
Unity Catalog is powerful, but setting up fine-grained access control across data, schemas, and workspaces can become tricky, especially in larger organizations. The UX/UI of some parts of the platform feels polished, others less so.
What problems is the product solving and how is that benefiting you?
Databricks is essentially solving fragmentation and inefficiency across the data lifecycle and the benefits come from removing a lot of friction between teams, tools, systems and data.
Unified Platform with Powerful Features, Needs Faster Cluster Startups
What do you like best about the product?
I appreciate how Databricks brought everything onto one unified platform, allowing our teams to collaborate in shared notebooks and ensuring data consistency with Delta Lake's ACID transactions. My favorite feature is Auto Loader, which automatically ingests new data files as they land in cloud storage, saving our team 2-3 hours a week on manual pipeline monitoring. Unity Catalog has been a game changer for us, providing a central place for governance and access control, which before was a mess. The initial setup was straightforward, and we had our first cluster and notebooks connected to S3 within a day, which was impressive given the platform's power. The workspace configuration and cloud integration guides are solid to follow.
What do you dislike about the product?
The cluster startup time is something that still catches us off guard. Cold start can take anywhere from 3-5 minutes, which gets frustrating when you are in the middle of an iterative debugging session and just need to test a quick fix. The cost management also needs some upgrades as currently the billing dashboards are improving but it still takes some digging to pinpoint exactly which job or user is driving up spend.
What problems is the product solving and how is that benefiting you?
I use Databricks to unify our data processing and machine learning, reducing pipeline delivery delays by 40%. It enables team collaboration with consistent data, saving hours with the autoloader, and simplifies governance with Unity Catalog.
Great UI and a Straightforward, Linear Learning Curve.
What do you like best about the product?
The UI is great compared to other providers. It’s easy to work with, and the learning curve feels linear and straightforward.
What do you dislike about the product?
Consumption-based costs are on the higher side, and it can be difficult for users who aren’t proficient in Python or Spark.
What problems is the product solving and how is that benefiting you?
A centralised data warehouse, with notebooks running on top of it for further analysis and ML use cases.
Unified Platform with Scalability and ML Power for Big Data
What do you like best about the product?
I like Databricks for its unified platform, which brings data engineering, analytics, and machine learning together. It simplifies workflow scaling and is easy for handling big data. The collaboration across the team is much smoother, which I really appreciate.
What do you dislike about the product?
I would say cost transparency maybe. User-based pricing can be hard to predict. So the initial setup and cluster configuration can feel complex. Better documentation for that and UI could be more intuitive in some areas.
What problems is the product solving and how is that benefiting you?
I use Databricks to sort ETL pipelines, handle large-scale data efficiently, reduce data processing time, and eliminate data silos. The unified platform improves collaboration between data engineers and scientists, simplifying workflows and making big data management smoother.
Seamless Integration and Scalable Performance with Room for UI Improvement
What do you like best about the product?
I use Databricks to build ETL pipelines and process large-scale data with Spark. I like Databricks most for its seamless integration with Apache Spark, collaborative notebooks, and its ability to handle large-scale data processing efficiently in a unified platform. The seamless Apache Spark integration lets me process huge datasets quickly without worrying about cluster setup, while collaborative notebooks make it easy to work with my team in real-time. The scalable architecture ensures reliable performance even with heavy data workloads. The initial setup of Databricks was fairly straightforward, especially with cloud integration.
What do you dislike about the product?
The UI can feel a bit cluttered at times, cluster startup times can be slow, and the pricing can get expensive for smaller projects or prolonged usage.
What problems is the product solving and how is that benefiting you?
I use Databricks to efficiently process large-scale data, simplify ETL workflows, and collaborate with my team in a unified environment, gaining faster data-driven insights.
Powerful Unified Analytics with Seamless Governance and Effortless Scaling
What do you like best about the product?
What I like best about Databricks is its powerful and unified analytics ecosystem. Features like Unity Catalog and Metastore make data governance and access control seamless, while the Lakehouse architecture combines the best of data lakes and warehouses. PySpark support, dbutils, and collaborative workspaces make development efficient, and serverless compute simplifies scaling without infrastructure overhead.
What do you dislike about the product?
What I dislike about Databricks is the slow startup time of all-purpose clusters, which can interrupt workflow and reduce productivity. Additionally, Git integration can feel a bit sluggish at times, especially during commits or syncing, making version control less seamless than expected.
What problems is the product solving and how is that benefiting you?
Databricks solves the challenge of managing end-to-end data workflows by providing a unified platform for data engineering, data science, and analytics. It allows seamless data processing, transformation, and model development within a single environment.
This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable.
This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable.
Unified Data Platform, Minor Cost and Complexity Challenges
What do you like best about the product?
I like that Databricks provides a unified platform for data engineering and data science, eliminating friction across teams and enhancing the ability to accelerate development and deployments. It works especially well for end-to-end CICD pipelines.
What do you dislike about the product?
Well, in terms of what can be improved, I think, perhaps the cost management. If this can be looked into to make it more cost efficient for users, it will go a long way. And in addition to that, operational complexity sometimes presents a complex platform for new users to navigate easily. So if this can be addressed, then I think it should be a lot easier for engineers to work with.
What problems is the product solving and how is that benefiting you?
I use Databricks for scalable workflows across multi-cloud environments, solving data silo unification and minimizing bottlenecks in complex data processing. It optimizes cost and governance while providing a collaborative workspace, real time data ingestion, and enhanced system reliability and performance.
Unified Data Workflows with Databricks
What do you like best about the product?
I really like Databricks for its collaborative lake house environment, which has been key in unifying our data engineering and machine learning workflows. It bridges the gap between our engineering and analytics teams, allowing us to run BI and AI on a single platform. Additionally, the initial setup was surprisingly fast from a workspace perspective, especially with the native integration in Azure.
What do you dislike about the product?
The learning curve is quite steep for non-engineers. We've also had to be very diligent with cost monitoring as auto scaling clusters quickly lead to unexpected expenses if not managed strictly.
What problems is the product solving and how is that benefiting you?
Databricks solved our data stack fragmentation by unifying storage lakes and warehouses. It bridged the gap between engineering and analytics, letting us run BI and AI on a single platform.
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