Databricks Data Intelligence Platform
Databricks, Inc.External reviews
763 reviews
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External reviews are not included in the AWS star rating for the product.
Unified ML Platform That Removes Infrastructure Friction
What do you like best about the product?
The unified platform experience is genuinely hard to beat — having MLflow for experiment tracking, Unity Catalog for governance, vector search, and serverless endpoints all in one place removes so much infrastructure friction. Feature engineering pipelines and model deployment feel cohesive rather than stitched together. The SQL warehouse + notebook hybrid workflow also makes it easy to hand off between data engineering and ML work without context switching tools.
What do you dislike about the product?
Serverless endpoints have some sharp edges — Spark context initialization behaves differently than in interactive clusters, which can cause silent failures if you're not careful about where you initialize things. Cold start latency on serverless is also noticeable for low-traffic production endpoints. Documentation around some of the newer features (like vector search index configs) tends to lag behind the actual product behavior, so you end up doing a lot of trial and error.
What problems is the product solving and how is that benefiting you?
We use Databricks to consolidate ML model development, feature engineering, and deployment for a cards and payments platform — work that previously required juggling separate tools for data processing, training, and serving. The unified environment means our ML engineers can go from raw transaction data to a deployed churn prediction model without leaving the platform. MLflow tracking keeps experiments reproducible, and Unity Catalog gives us the data governance story our banking client needs. It's cut down a significant amount of the coordination overhead that comes with multi-tool ML pipelines.
From 1 Hour to 10 Minutes: How Databricks Modernized Our Workflow
What do you like best about the product?
We used to use ADF to get data from SQL Server and then work on it in Databricks before putting it into Salesforce. The whole process took a time more than an hour because ADF added extra work.
Now everything happens inside Databricks. We transform the raw data in Databricks and put in into Salesforce all in one place. This has made the whole process much faster, it now takes 10 minutes. That is an improvement from what we had with ADF.
Delta Lake has also been really useful. It helps us keep track of changes and go back if something goes wrong. We can see what happened before . Fix mistakes easily.
Delta Lake also makes sure the data is good before it goes into the pipeline. It stops data from getting in and causing problems later on in Salesforce. This makes the whole process more reliable and easier to take care of.
Now everything happens inside Databricks. We transform the raw data in Databricks and put in into Salesforce all in one place. This has made the whole process much faster, it now takes 10 minutes. That is an improvement from what we had with ADF.
Delta Lake has also been really useful. It helps us keep track of changes and go back if something goes wrong. We can see what happened before . Fix mistakes easily.
Delta Lake also makes sure the data is good before it goes into the pipeline. It stops data from getting in and causing problems later on in Salesforce. This makes the whole process more reliable and easier to take care of.
What do you dislike about the product?
Databricks is really good at what it does.. Sometimes it takes a while to get the cluster up and running.. The user interface is slow at sometimes. This can be annoying when we are in a hurry to get things done for Salesforce. The Salesforce connectors in Databricks can be a bit tricky to work with. They often need to be set up right and do not work as we expect. This means we have to put in work when we are trying to figure out problems or keep an eye on the pipelines, in Databricks for Salesforce.
What problems is the product solving and how is that benefiting you?
It is solving our performance and reliability issues - by allowing us to extract, transform and load the data into Salesforce all in one place without ADF. This unified workflow has reduced our runtime from 1 hour to 10 minutes giving us faster job completion and on time Salesforce data updates.With delta lake features like ACID transactions and time travel,our data is more accurate and easier to recover when something goes wrong.
Centralized Dashboard with Smooth, Cost-Saving Autoscaling
What do you like best about the product?
Everything is centralized is a single dashboard spark jobs, notebooks and data pipelines. Autoscaling and auto termination genuinely help keep costs under control, and we could was a pleasant surprise that both run smoothly without any noticable lag. Sharing notebooks with the team is straightforward and cuts down on alot of back and forth.
What do you dislike about the product?
Finding older queries is really paunful. Anything beyond a few weeks becomes hard to track down, which makes it difficult to keep my data to day work flowing smoothly and to continue working without constant interruptions.
What problems is the product solving and how is that benefiting you?
We run ETL and ML workloads without having to worry too much about the underlying infrastructure. I can also manage inventory information, at least to some extent, without opening a bunch of different tabs. I spend less time troubleshooting clusters and more time actually working with the data.
Unifies Data Processing with Delta Lake's Reliability
What do you like best about the product?
I use Databricks in my enterprise environment and projects to ingest data from multiple sources, transform and clean it at scale, and prepare reliable datasets for analytics and reporting. It allows me to build and manage data pipelines efficiently using Spark, SQL, and notebooks. I love having data ingestion, large-scale processing, analytics, and collaboration all in one place, making my workflow much more streamlined and efficient. I really value the reliability and confidence I get from features like Delta Lake, which make data versioning, recovery, and handling changes much safer, cheaper, and easier in my projects. Delta Lake is one of the main reasons Databricks is so valuable to me because it directly addresses reliability and trust, which are constant challenges in real data projects. The ability to rollback to a previous version if something goes wrong makes me much more confident when developing, testing, or deploying changes to production pipelines. Additionally, the initial setup was relatively straightforward because Databricks integrates well with our existing cloud infrastructure.
What do you dislike about the product?
The learning curve can be quite steep at the beginning, especially for users who are new to Spark or large scale data processing concepts. Debugging complex pipelines or job failures can sometimes be time-consuming, when error messages are not very intuitive. As workflows and environments grow, governance and environment management can require extra effort to keep everything well-organized and consistent. Cost management is another challenge, as resource usage can increase quickly if clusters and jobs are not configured or monitored carefully.
What problems is the product solving and how is that benefiting you?
I use Databricks to solve fragmentation and inefficiency in my data flow, handling ingestion, transformation, analytics, and collaboration on one platform. It reduces operational overhead, ensures data quality, and offers scalability, improving large data processing without infrastructure worries.
Scalable, Unified Platform with a Steep Learning Curve
What do you like best about the product?
I use Databricks for my office projects, and I really like its ability to unify the entire data workflow in a single platform. It lets me seamlessly collaborate with data scientists and analysts, making it easy to ingest, clean, analyze, and model data. I appreciate its scalability and automation features, which save me time and reduce complexity when working with large datasets. I also like that it offers a scalable compute and storage solution, reducing infrastructure management overhead. The integration of shared notebooks and tools like Databricks Genie helps improve collaboration and speed up development.
What do you dislike about the product?
I haven't faced major issues with Databricks itself, but during my initial phase of using the platform, it wasn't very easy to get up to speed with all the features, tools, and configurations. Databricks evolves quickly and in the beginning, it was a bit challenging to match the pace of updates and fully leverage all its capabilities. The initial setup was moderately challenging. While the platform is well documented and user-friendly, getting familiar with all the features, configuring clusters and integrating it with our existing workflows required some learning and experimentation.
What problems is the product solving and how is that benefiting you?
Databricks solves scalability and performance issues, centralizing data from multiple sources and reducing silos. It simplifies collaboration among data professionals, offers scalable compute, and integrates advanced analytics, saving time and reducing complexity with large datasets.
Versatile Data Platform with Seamless Integration
What do you like best about the product?
What I like most about Databricks is that it's integratable with other platforms. I can literally set up a Databricks workspace using Azure data services from the Azure portal, and I can also use Databricks within AWS. It gives me the opportunity to integrate my Databricks notebooks into other environments and orchestration tools or ETL tools, like Azure Data Factory.
What do you dislike about the product?
For now, I noticed when I'm using Azure Databricks, particularly the Azure Databricks cluster, it usually times out, and it's kind of frustrating for me. Most times when I'm working, I just go into another tab. Every time I come back in a minute or two, it's timed out, and I have to sign in again. That experience can be frustrating. I would like that to be looked into. I don't know if it's an issue with Databricks or if it's an issue from the Azure side from the intraident authentication part of things.
What problems is the product solving and how is that benefiting you?
I use Databricks to unify my data by managing governance within the Unity catalog, simplifying user access and report sharing.
Databricks Makes Collaboration and Reliable Data Pipelines Easy
What do you like best about the product?
I really enjoy working in the Databricks environment because it makes it easy to collaborate with others through shared notebooks. Delta Lake technology has also been great for ensuring data quality and reliability across our pipelines. It lets us manage data, build pipelines, and run AI/BI workloads all in one place.
What do you dislike about the product?
The interface is quite laggy at times, especially when I’m scrolling through a notebook or spinning up a cluster.
What problems is the product solving and how is that benefiting you?
Because it’s a unified, end-to-end platform covering everything from data ingestion and transformation to AI and BI insights, it enables faster analysis and helps convert complex datasets into actionable decisions more efficiently
Robust Infrastructure and Easy Setup
What do you like best about the product?
I think the infrastructure in Databricks is really useful, especially its facilities and usage for an admin handling workspaces and client issues. It was easy to use token access, which made setup possible and fairly simple. I also appreciate how daily use in my community involves about 120-200 people. I find Databricks' infrastructure valuable in supporting my role effectively.
What do you dislike about the product?
I think it would be better if Databricks could send people alerts when any new features come to the market.
What problems is the product solving and how is that benefiting you?
I use Databricks to manage workspaces and client issues efficiently. It's easy to use, especially with token access management, facilitating smooth operations. The infrastructure and features make handling queries and workspace access straightforward, improving overall administration.
Databricks Genie and AgentBricks Make “Talk to Data” Easy
What do you like best about the product?
In databricks I like genie and agentbricks that help me to solve business process as talk to data
What do you dislike about the product?
I think all the functionality works as expected for me.
What problems is the product solving and how is that benefiting you?
It’s mainly about giving my business users more flexibility to talk directly to the data and run their own analysis without needing to know SQL.
Databricks Saves Time with Smooth, High-Performance Data Pipelines
What do you like best about the product?
Databricks saves time by automating data pipelines, improving performance, and reducing infrastructure management.
Overall, it provides a smooth experience for building, analyzing, and deploying data solutions.
Overall, it provides a smooth experience for building, analyzing, and deploying data solutions.
What do you dislike about the product?
Databricks provides strong capabilities for large‑scale data processing and collaboration, but there are areas for improvement.
What problems is the product solving and how is that benefiting you?
We use Databricks for building and managing large‑scale data pipelines and analytics workloads.
It helps us process high‑volume data faster by using scalable Spark clusters and automated workflows.
It helps us process high‑volume data faster by using scalable Spark clusters and automated workflows.
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