Databricks Data Intelligence Platform
Databricks, Inc.External reviews
672 reviews
from
and
External reviews are not included in the AWS star rating for the product.
Severe Pricing Miscalculations and Inadequate Remediation
What do you like best about the product?
The platform itself is relatively easy to use and quick to stand up. Initial setup was straightforward, and core workflows were accessible without excessive configuration. From a purely technical standpoint, the product is usable and well-designed.
What do you dislike about the product?
Databricks needs to take real accountability for pricing accuracy and post-sale remediation. Their usage and pricing calculator was materially wrong for our use case (off by ~10x), and we raised concerns early. We never received the level of attention or corrective action needed to resolve it properly.
We ended up paying more than 10x the expected cost, with only the final quarter partially rebated, which is not a meaningful remedy. Databricks declined responsibility and instead referenced emails sent after the contract was signed, which did not align with when the issue was identified.
This experience has made us seriously reconsider alternatives like Snowflake. My advice to others: be extremely cautious with Databricks’ pricing estimates and assume you will bear the risk if they are wrong. Once the contract is signed, resolution is difficult, and revenue protection appears to take priority over customer outcomes.
We ended up paying more than 10x the expected cost, with only the final quarter partially rebated, which is not a meaningful remedy. Databricks declined responsibility and instead referenced emails sent after the contract was signed, which did not align with when the issue was identified.
This experience has made us seriously reconsider alternatives like Snowflake. My advice to others: be extremely cautious with Databricks’ pricing estimates and assume you will bear the risk if they are wrong. Once the contract is signed, resolution is difficult, and revenue protection appears to take priority over customer outcomes.
What problems is the product solving and how is that benefiting you?
Databricks Data Intelligence Platform helps by crunching the numbers, making it easier for us to handle data analysis for our machine learning models.
Outstanding All-in-One Analytics with Intuitive UI and Impressive Speed
What do you like best about the product?
I like that it's fast, and excellent all-in-one analytics solution, with excellent scalability that reduces time to market. The user interface is intuitive and perfect for users with varying skill levels. I also like that the database has the ability to terminate or time out instances, which helps us manage costs.
What do you dislike about the product?
I have no complaints about this tool; it allows us to run code efficiently without getting bogged down by infrastructure or optimization concerns. Additionally, there is a wealth of helpful training resources available for both developers and data scientists.
What problems is the product solving and how is that benefiting you?
Databricks serves as our main data platform, allowing us to collect, standardize, clean, transform, and refine our various data sources. Its workflow capabilities have enabled us to automate repetitive tasks and build internal applications using reusable workflows.
Powerful unified data platform with great collaboration
What do you like best about the product?
Databricks is easy to use and simple to get started with, even when handling large amounts of data. It brings everything like data processing, analytics, and AI into one place, so my team do not need multiple tools. The platform integrates smoothly with common tools like BI dashboards, workflows, and cloud services. Its notebooks make day to day work frequent and convenient by allowing our teams to collaborate in real time. There are many built in features for data, analytics, and machine learning without added complexity. Customer support, documentation, and community resources make it easier to solve issues quickly.
What do you dislike about the product?
Databricks can be expensive and unpredictable in cost, especially for small teams if workloads run longer than expected. It takes technical expertise to set up, manage, and optimize performance, which can be challenging for non-technical users. Costs need frequent monitoring since compute and storage are billed separately. While it has basic dashboards, it still depends on tools like Power BI or Tableau for full reporting.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of managing data across many different tools by putting everything in one place. This saves time, reduces confusion, and makes it easier for teams to work together. It helps us handle large data smoothly, keep access and security under control, and get useful insights faster even for people who are not very technical.
Unified Data Platform That Simplifies Complex Workflows
What do you like best about the product?
What I like best about Databricks is how it brings data engineering, analytics, and machine learning together on one platform. Having a unified environment built around Apache Spark makes collaboration between data teams much easier. The Lakehouse approach works well because it removes the need to move data across multiple tools. Performance is strong for large datasets, and notebooks make experimentation, analysis, and collaboration more efficient. Overall, it simplifies complex data workflows while still being powerful.
What do you dislike about the product?
The main downside is the cost and complexity for new users. Pricing can be hard to predict, especially when workloads scale unexpectedly, and compute costs can rise quickly if not monitored closely. There is also a learning curve for teams that are not already familiar with Spark or cloud-based data platforms. Some advanced configurations and optimizations require experienced resources, which can slow adoption for smaller or less mature data teams.
What problems is the product solving and how is that benefiting you?
Databricks helps us solve the problem of working with large, fragmented datasets across different tools and teams. Earlier, data engineering, analytics, and machine learning were handled in separate systems, which created silos and slowed down insights. With Databricks, we can process, analyze, and model data on a single platform, which improves collaboration and reduces data movement.
From a business perspective, it helps us generate insights faster, scale analytics as data grows, and improve data reliability. This leads to quicker decision-making, more consistent reporting, and better use of data for forecasting and optimization, while reducing operational overhead.
From a business perspective, it helps us generate insights faster, scale analytics as data grows, and improve data reliability. This leads to quicker decision-making, more consistent reporting, and better use of data for forecasting and optimization, while reducing operational overhead.
It is a very expensive cloud stack but it does deliver the right performance along with it
What do you like best about the product?
Databricks Data Intelligence Platform is very reliable and that is nice to know that cloud native architecture did not go down right after I deployed it on kubernetes. Honestly, I thought the python/r integration would be busted so that was a shock to find that both ran with out any lag.
What do you dislike about the product?
Those license prices are criminal for what you receive. Additionally, the “cas server” management of the product has been an absolute pain to try and figure out. I spent two hours just trying to understand how to use the sessions. There is no straight shot of getting help from the documentation or google.
What problems is the product solving and how is that benefiting you?
That saves me from having to load up heavy software onto my laptop because it loads into the browser. I am able to at least somewhat manage the inventory information without opening 10 different tabs.
The smoothest my big data work has ever felt
What do you like best about the product?
I mostly use the Databricks Data Intelligence Platform to mangle large datasets that we store across cloud buckets and create etl pipelines, as well as stand up notebooks on which I do a lot of explorative work. I very much like that everything feels ready to go such as clusters start quickly, scaling just works in the background and I can really stop worrying about infrastructure stuff and focus on analysis.
What do you dislike about the product?
The UI can feel slow especially when I’m deep in the middle of a heavy notebook session and sometimes things are just SLOW to click or jobs don’t cancel when I ask it to. And I’ve only just started messing around with the cluster software actually, and while it’s powerful there’s definitely a learning curve as I still dig through stuff on occasion trying to figure out which cluster or runtime setting is making things run differently than others.
What problems is the product solving and how is that benefiting you?
Databricks has, quite literally, taken the hassle out of dealing with big data infrastructure and instead of waiting for clusters to spin up and spending time hunting down why jobs failed, I can open a notebook and start working. That change alone has accelerated our etl development, lowered our cloud expenses significantly and made it far less risky to experiment with ml workflows without having to think about scaling.
Powerful Unified Platform for Data and AI, but Complex Setup and Costly for Continuous Use
What do you like best about the product?
I like that Databricks provides a unified environment for data engineering, analytics, and machine learning. The platform makes it easy to collaborate across teams, manage large-scale data efficiently, and build advanced AI models using the same infrastructure. The integration with major cloud providers and the Lakehouse architecture make data management both flexible and scalable.
What do you dislike about the product?
While Databricks is a powerful and flexible platform, it can be complex to set up and manage, especially for teams without strong data engineering expertise. The cost structure can also become expensive for continuous workloads, and performance tuning sometimes requires deep knowledge of Spark and cluster optimization. Additionally, the user interface could be more intuitive for non-technical users.
What problems is the product solving and how is that benefiting you?
Databricks helps centralize and manage large volumes of data from different sources in a single, scalable platform. It simplifies data processing, analytics, and machine learning workflows, allowing teams to collaborate efficiently and deliver insights faster. By integrating data engineering, analytics, and AI capabilities, it reduces infrastructure complexity and accelerates the development of data-driven solutions.
Effortless Data Management and Fast Analytics Set This Tool Apart
What do you like best about the product?
Managing large amounts of data while working in coordination with other teams for systematic data uploads, updates, and troubleshooting is seamless, without concerns about limitations. Additionally, the speed at which analytics and geometric machine learning are processed truly sets it apart.
What do you dislike about the product?
beginners complexity in handling the tools in a test or trial phase
What problems is the product solving and how is that benefiting you?
It is so far has the capability of handling data in large quantity, being company's presence across several states it makes the data compilation easy for the department to systematically get the update
one of leaders in data
What do you like best about the product?
What I really like about the Databricks Data Intelligence Platform is how it brings everything together in one place. Instead of juggling different tools for data engineering, analytics, machine learning, and governance, you can do it all in a single environment.
What do you dislike about the product?
Honestly, what I find a bit frustrating about the Databricks Data Intelligence Platform is that while it’s incredibly powerful, it can also feel overwhelming at times. There’s a steep learning curve, especially for teams who are just getting started and don’t have much experience with Spark or distributed systems.
What problems is the product solving and how is that benefiting you?
The big problem it solves is breaking down data silos. Instead of having separate systems for raw data, analytics, and machine learning, Databricks gives you one platform where everything connects. That means less time moving data around and more time actually using it.
Databricks review
What do you like best about the product?
What I like the most is the ease with which I can work with data from different sources in one place. Before, I had to jump between various tools, but here everything is integrated: from ingestion to advanced analysis.
What do you dislike about the product?
What I like least is that, at the beginning, the learning curve can be a bit steep.
What problems is the product solving and how is that benefiting you?
It has made it easier to reduce information processing times, which speeds up strategic decision-making, but a challenge is that with so many features, it can initially feel complex to navigate the platform and know exactly what to use.
showing 31 - 40