Databricks Data Intelligence Platform Reviews & Product Details

I am glad my leg is not completely dead from having to wait for a query (at least the auto termination feature works and saved me money) History is somewhat useful I rely on it to pull up previous scripts I write because I always seem to forget what I did the day before. I think it helps keep finance off my back by saving them money on the AWS bill. To me, it's simply one central location for managing our big data so I do not have to watch 5 monitors. Review collected by and hosted on G2.com.

If I want to find a query that was run over 30 days ago the search function does not even come close to being able to help me and also installing R Packages is a complete nightmare and the error messages are as clear as mud. And when working with large amounts of data the user interface becomes sluggish and trying to scroll through a notebook is like pushing through the mud. It would be nice if it were easier to install/ manage external spark packages. Review collected by and hosted on G2.com.

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.

What I like best about the Databricks Data Intelligence Platform is how it brings everything data engineering, analytics, and machine learning together in one unified environment. It’s very user-friendly despite being powerful, and it makes collaboration between technical and non-technical teams much easier. The platform handles large volumes of data efficiently, scales smoothly, and integrates well with cloud services, which saves a lot of time and effort. Overall, it helps turn raw data into meaningful insights faster without making the process overly complex. Review collected by and hosted on G2.com.

One thing I dislike about the Databricks Data Intelligence Platform is that it can feel complex and overwhelming for new users, especially those without a strong technical background. The learning curve is quite steep, and it takes time to fully understand how to use all the features effectively. Additionally, the pricing can become expensive as usage scales, and cost management isn’t always very transparent. Sometimes, debugging errors or performance issues can also be challenging, particularly in large or highly integrated workflows. 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.

Databricks is a Data Analytics tool that is used by our designing engineers to analyse all the different type of financial and operational data of our company and create a summary of everything so that understanding of companies workflow can be simple. Review collected by and hosted on G2.com.

One of the major disadvantage of databricks is that the software is completely based on programming language like Python, SQL, Scala so if the user does not have knowledge of these programming languages than it is very difficult for them to use this platform. 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 like how Databricks brings data engineering, analytics, and AI into a single platform. The Spark and Delta Lake integration is seamless, performance is excellent, and collaboration through notebooks is very smooth. Its tight integration with Azure services also makes data pipelines, security, and scaling much easier. Review collected by and hosted on G2.com.

The main downside is the cost, especially when running large or long-running workloads. Debugging complex Spark jobs can also be challenging at times, and fine-tuning performance often requires a good understanding of Spark internals. Review collected by and hosted on G2.com.

I like the integration between Spark, Delta Lake, and notebooks, which helps optimize and scale data pipelines. And the other feature that I love is the different cluster options that we have, which are really easy to tune for cost and performance. Review collected by and hosted on G2.com.

Debugging complex Spark jobs and understanding some of the optimization features is a time taking process in Databricks, is what I think. Review collected by and hosted on G2.com.