Databricks Reviews & Product Details

I love how easy it is to integrate multiple data workloads such as Data Warehouse, Data Lakes, and Model Registry for the organization in a single interface with Databricks. Review collected by and hosted on G2.com.

I think working with Catalog still requires a different tab open. If we can integrate it into the multiple tabs section within Databricks UI, it would reduce context switching and would help users to stay focused and productive. 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 like how Databricks allows not just engineers, but also data managers, analysts, data scientists, and everyone to work in a simplified and collaborative manner. That's a feature I appreciate which Databricks does well, setting it apart from competitors who are trying to offer similar capabilities. Many people have already adopted it, and it has become the de facto choice. Review collected by and hosted on G2.com.

I think the lineage and the addition of business assets, as well as how the data translates to the business layer of the bank or any other vendor, is where Databricks can improve. I don't see different departments getting connected in Databricks by the glossary or items which they use for themselves. Review collected by and hosted on G2.com.

I like the overall environment, especially the governance features and the way the UI is handled. I primarily use Databricks as my ETL platform, and it fits well with how I work. The SDP job management governance and lineage capabilities are also helpful. Review collected by and hosted on G2.com.

Sometimes there are glitches in the UI. For example, if I cancel something, it takes a bit longer for that change to be reflected in the UI. Review collected by and hosted on G2.com.

Databricks Lakehouse Platform is an ultimate solution for big data analytics that helps organizations analyze large amounts of data quickly.

Key features of the Databricks Lakehouse Platform include data processing, data warehousing, and machine learning capabilities into a single platform, this also includes security and governance features

Overall, the Databricks Lakehouse Platform is a powerful solution for organizations that need to analyze large amounts of data. Review collected by and hosted on G2.com.

There is nothing to dislike about the data bricks lakehouse platform. But the no of file formats can be increased in addition to the Parquet format. 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 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. Review collected by and hosted on G2.com.

I think it would be better if Databricks could send people alerts when any new features come to the market. Review collected by and hosted on G2.com.