Databricks Data Intelligence Platform Reviews & Product Details

The autoscale works well; it also helped us reduce the cost of using cloud resources. I thought that was going to be a problem since this is the first time we used autoscale. The support has been good enough, they normally appear on time for their scheduled hours to assist us in fixing the problems we create. The ability to save the query history in order of how the queries were written is nice as I often forget what I write a few minutes after writing it. When working with coworkers who need access to your code you can send them the permalink (link) to the code which is better than having to explain it. Since it supports both Spark and Presto within one tool I do not have to jump between tools. Review collected by and hosted on G2.com.

I hate the way the search function works. I have never found the results from a month ago, and it is annoying. The UI will occasionally lag behind my typing, and if I am in a rush I feel it takes too long. Also it would make much more sense for the tables drawer to remain open when I click on the notebook and instead of closing automatically which is very time consuming as I need to reopen it again. I have had the support team tell me they cannot replicate an issue which is no help at all. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I have used approx. number of Data Analytics platform but the user friendly enviornment and features of Databricks gives always reliable and satisfactory experience to me that gives a confidence to handle large data sets without any problem. Review collected by and hosted on G2.com.

Never have any issue with their services instead they gives a best user support to us to manage all our AIML integrations and new AI implementations. Review collected by and hosted on G2.com.

Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze. Review collected by and hosted on G2.com.

The initial setup was a bit confusing, and some of the advanced features could use better documentation. Figuring out the pricing took some time, but once we got going, the benefits were clear. Review collected by and hosted on G2.com.

I am using data bricks from around 1 year. In a week I use it approx 3-4 days. Everything is integrated, which means I don’t have to switch between multiple tools to do different tasks. It really improves team collaboration. Sharing notebooks and collaborating on models is super easy. This has been great for our team since we often work together on projects and need to see each other's code and progress. Review collected by and hosted on G2.com.

This is probably the biggest downside. Databricks can get pretty expensive, especially if you’re not monitoring your usage carefully. It’s definitely a platform that’s best suited for teams with a larger budget or organizations that need heavy-duty data processing. Review collected by and hosted on G2.com.

This tool can be implemented on nearly any platform and provides a wide array of integrated analytical features. It offers a thorough set of analytical functions that make operations easier and deliver a comprehensive perspective on the technology. Its performance in big data analytics is outstanding, as it is built to handle and process vast quantities of information efficiently. Review collected by and hosted on G2.com.

It requires a bit more time for a database administrator and is somewhat difficult to configure when migrating infrastructure. Sometimes some adjustments are necessary to find the exact solution. Review collected by and hosted on G2.com.

This platform is excellent for scaling large language models (LLMs) and generative AI applications. It offers a unified location to manage security and access data files across multiple cloud environments. Review collected by and hosted on G2.com.

Databricks is designed for handling large data sets, so using it for smaller tasks can feel excessive. Review collected by and hosted on G2.com.