A typical use case for the solution is to build the data lakehouse for the client because they have a variety of source systems, and they want to unify that data into the lakehouse platform, where they want to use the data for analytical purposes and insights.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
637 reviews
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External reviews are not included in the AWS star rating for the product.
Very reliable platform for modern data projects in company !
What do you like best about the product?
One thing I genuinely appreciate about Databricks is its ability to understand and adapt to our organization's specific data terminology. The platform's natural language interface makes it feel like I'm conversing with a colleague rather than navigating complex queries. Additionally, the AI-driven insights have significantly accelerated our decision making processes, allowing us to act fast on data trends. Overall its a good tool with fine customer support
What do you dislike about the product?
While Databricks is powerful there is definitely a learning curve when you're just getting into it. Also interface can be improved for mobile.
What problems is the product solving and how is that benefiting you?
We used to struggle a lot with keeping our data pipelines reliable , things would break in silos, and teams had no clear visibility. Databricks really helped us unify our data processes across departments. Now our different experts from seperate departments could easily work together . It is also helped in speeding up our model training workflows for our clients.
Centralised data management platform and excellent Ai features !
What do you like best about the product?
What i liked in Databricks is that it let us handle both raw and refined data in one go ,no need to bounce between tools. We are able to build pipelines, run analytics, and even go into ML models from the same environment.
The collaborative environment was excellent for our data engineers and analysts can work in real time without being lost in getting on same page . Also, the way it connects with AWS and Azure made onboarding a lot smoother for our hybrid cloud setup.
Honestly, the speed at which we can experiment and push insights to dashboards has helped us stay way more better than before. We are not just storing data anymore we’re using it intelligently without too much friction.
The collaborative environment was excellent for our data engineers and analysts can work in real time without being lost in getting on same page . Also, the way it connects with AWS and Azure made onboarding a lot smoother for our hybrid cloud setup.
Honestly, the speed at which we can experiment and push insights to dashboards has helped us stay way more better than before. We are not just storing data anymore we’re using it intelligently without too much friction.
What do you dislike about the product?
While Databricks brings a lot to the table, a few things still feel clunky. The UI especially when dealing with larger clusters i do find some little lag sometimes other than that i find no major problem.
What problems is the product solving and how is that benefiting you?
Before Databricks we were switching between multiple tools to clean, store, and analyze data . Which slowed everything down and made collaboration tricky. Databricks pretty much merged all of that under one roof.
One of the biggest wins has been the ability to process large datasets without worrying about performance bottlenecks. We’re now running real-time analytics that used to take hours, especially for sales and operations dashboards.
Also, their unified platform means our data engineers, analysts, and ML folks can actually work together without throwing files back and forth. It’s helped us push insights to business teams faster, and decisions are now backed by fresher, cleaner data.
One of the biggest wins has been the ability to process large datasets without worrying about performance bottlenecks. We’re now running real-time analytics that used to take hours, especially for sales and operations dashboards.
Also, their unified platform means our data engineers, analysts, and ML folks can actually work together without throwing files back and forth. It’s helped us push insights to business teams faster, and decisions are now backed by fresher, cleaner data.
Data and AI for everyone
What do you like best about the product?
Easy to use, but still technical enough.
What do you dislike about the product?
Still not the market leader, not public.
What problems is the product solving and how is that benefiting you?
Sharing data, ETL, Extracting value from it via dashboards for managers.
Good holistic platform starting big data all the way to latest AI
What do you like best about the product?
A platform that host wide varieties that can be used for for both Data Lake and Warehouse purpose
What do you dislike about the product?
Complexity in making things works, especially cluster management
What problems is the product solving and how is that benefiting you?
Started with the replacement of Hadoop & now many of Analytics practice offloaded to Databricks & Data Lake
Best Datalake
What do you like best about the product?
We moved to Databricks 3 years back and we are migrating our DW to Databricks using DLT. Love so far.
What do you dislike about the product?
Cost of serverless when we use serverless the cost spiked like 30%
What problems is the product solving and how is that benefiting you?
We use Databricks for DW and source for powerbi reports
Databricks for Data analysis
What do you like best about the product?
In this platform bulk data analysis is easy because this runs on Data intelligence engine which has generative Ai feature helps understand and process data. Databricks Assistant is very helpful.
What do you dislike about the product?
The interface is not good also pricing is not efficient.
What problems is the product solving and how is that benefiting you?
Data analysis, this tool is best and it solved bulk data analysis problem.
Databricks Simplifies Data Processing and AI Integration
What do you like best about the product?
Lakehouse architecture combines the best of data lakes and warehouses, offering ease of use, seamless integration and eliminating the need for complex data pipelines.
What do you dislike about the product?
Databricks is powerful but can be difficult for beginners, especially those unfamiliar with big data tools or SQL.
What problems is the product solving and how is that benefiting you?
Big data processing has become much more efficient. We now handle large volumes of data in real time, which allows us to generate AI-driven insights faster and make smarter business decisions.
Unifying data for analytical insights with smooth AI and machine learning integration
What is our primary use case?
What is most valuable?
The most valuable features of Databricks are especially the Delta Lake and the Unity Catalog; those are the main features. The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse. Currently, they're coming up with workflow jobs, along with other supporting elements to create an end-to-end solution.
What needs improvement?
In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until recently, everyone used third-party systems such as Power BI to connect to Databricks for dashboards and reports, but they're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further.
For how long have I used the solution?
I have approximately four years of experience working with Databricks.
What do I think about the stability of the solution?
I would rate the stability of Databricks as highly stable, around nine out of ten.
What do I think about the scalability of the solution?
I would rate the scalability of this solution as very high, about nine out of ten.
How are customer service and support?
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features. For us, it's so far so good with no problems, and I would rate the support quality as eight out of ten.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup of the Databricks solution is reasonably fair enough. It doesn't give any trouble to implement the solution, and I think it's fairly easy to set up and work on Databricks.
What was our ROI?
I can't say if there's seen an ROI from the solution because I do not have exposure in that area, although I think the people who decided to implement Databricks might have done all this analysis and POCs.
What other advice do I have?
My relationship with the vendor is that I'm not a partner of Databricks; I work for a client where we use the Databricks software for implementing the solutions.
My clients are usually enterprise-level organizations, but the area where they're implementing is medium level here, although it might go into enterprise level in the future.
Regarding the price of Databricks, I don't involve myself in those decisions.
I think Databricks is very good at facilitating AI and machine learning projects; they implement AI and machine learning models very well, and clients can run their models on Databricks. I believe they are in a better place compared to competitors such as Snowflake, and they are tying up with important companies such as SAP and Palantir.
Based on my experience, I would recommend Databricks to other people. Overall, I would rate this solution as one of the best, about eight out of ten, although I might not know some of the pitfalls; it's based on use case to use case, but for us, it's working well.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Databricks make enterprise scale ML project easier to manage.
What do you like best about the product?
We have build a fraud detection system for a European finntech product using Databricks Data Intelligence Platform. The project required ingesting large volume of transaction data, cleaning it and training multiple machine, learning models using historical fraud patterns. Feature like tight integration with ML flow, alone helped us avoid the usual mess of managing models across Juypter notebooks and cloud storage. It’s collaborative environment allowed our ML engineers and data scientists to work together in Databricks notebooks in the same interface. Additionally, the ability to schedule retraining jobs made it easier to put a model into production with minimum effort.
What do you dislike about the product?
While MLflow is great, the UI for comparing runs can feel a bit outdated and lacks advanced filtering options. Managing features stores also felt slightly inefficient without more granular access control for different user roles.
What problems is the product solving and how is that benefiting you?
Our ML pipeline is far more stable and efficient after we implemented Databricks. We have a standardised our development workflows and now our engineers, analyst and business teams can access the same datasets and results in a single environment. This has dramatically improved our team collaboration.
Great advanced analytical tool that utilises Spark to fullest
What do you like best about the product?
Its ability to combine big data processing with machine learning makes it possible to do advanced analytics and data engineering efficiently in one space. Its scalable design and collaborative workspace also make it simple for teams to work together and process large datasets without slowing down the system
What do you dislike about the product?
One downside of the Databricks Data Intelligence Platform is the steep learning curve for new users, especially when navigating complex features like Delta Lake and managing large-scale workloads
What problems is the product solving and how is that benefiting you?
It provides a unified environment for analytics, machine learning, and data engineering, addressing issues like managing massive datasets, scaling machine learning models and enables team collaboration. While collaborative notebooks improve teamwork, increasing productivity and speeding the implementation of data-driven solutions, its interaction with Apache Spark and Delta Lake guarantees effective data processing, consistency, and version control.
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