We use the product as a data science platform that enables me to handle and analyze large datasets efficiently.

Databricks Data Intelligence Platform
Databricks, Inc.External reviews
External reviews are not included in the AWS star rating for the product.
Provides seamless integration capabilities, but the cluster management features need improvement
What is our primary use case?
What is most valuable?
Databricks can switch easily between cloud providers, such as Azure and GCP. It allows seamless integration with various data platforms and cloud providers, facilitating better data handling and analysis.
What needs improvement?
The product could be improved regarding the delay when switching to higher-performing virtual machines compared to other platforms like Snowflake. The ease and speed of managing clusters can also be enhanced, especially when scaling up resources. They could add more advanced data storage solutions like Iceberg and Delta files.
For how long have I used the solution?
I have been using Databricks for approximately two years.
What do I think about the stability of the solution?
I rate the product stability a seven out of ten.
What do I think about the scalability of the solution?
I rate the product scalability an eight.
How are customer service and support?
The technical support services are good.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup was straightforward. However, configuring policies could have been simpler.
What's my experience with pricing, setup cost, and licensing?
The product pricing is moderate.
Which other solutions did I evaluate?
I evaluated other options, including Snowflake, before choosing Databricks.
What other advice do I have?
Databricks is a robust solution for big data processing, offering flexibility and powerful features. While there are areas for improvement, especially in performance and cluster management, it remains a highly valuable tool in my data science toolkit.
I rate it a seven.
Databricks platform
it has made the integration with different sources hassel free and we can more focus on data
ai assistant also provide incorrect suggestion few times
The new era of Databricks Data Intelligence Platform for all the AI solutions
Complete platform but a bit confuse
Process large-scale data sets and integrates with Apache Spark with notebook environment
What is our primary use case?
I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB.
What is most valuable?
Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.
What needs improvement?
While Databricks allows you to upload your packages, we encountered some limitations with its capabilities, particularly with Apache Spark, which also affected Databricks. We had issues working with spatial data. You had to go through many steps to find libraries that could process spatial data in a distributed fashion.
For how long have I used the solution?
I have been using Databricks since 2018.
What do I think about the scalability of the solution?
I might have a project that runs for one or two months, and perhaps I won't use it for six months. Self-service is one of its strengths. I can shut down everything and easily spin up resources when I need to use them again. We have a dedicated group of fifty people who consistently use Databricks for analytics.
How was the initial setup?
The initial setup was very easy and took around 10-15 people. We have a data science infrastructure team helping with this.
What was our ROI?
Databricks stands out among most data platforms mainly because of its ease of use. The learning curve is not as steep, making it accessible for anyone to handle large-scale data processing on Databricks. This ease of use contributes positively to our return on investment. However, in our line of work, converting this efficiency into direct monetary gains can be challenging, given our nonprofit nature.
What's my experience with pricing, setup cost, and licensing?
We purchased high-performance laptops to reduce our reliance on the cloud. The main issue was the cost. Internally, if I used Databricks, that cost would return to my team. There was a time when my monthly cost was around ten thousand dollars, which was quite high. Due to these costs, several teams, including ours, move away from using Databricks and other cloud providers. It became prohibitive, so we invested in our high-performance computers internally instead.
What other advice do I have?
Databricks provides ease of use for me, particularly due to its seamless integration with Apache Spark. This integration simplifies the process of conducting machine learning on large-scale datasets.
I recommend this solution 100%. Overall, I rate the solution an eight out of ten.
DataBricks Data Intelligence Platform Review
community to help as well
best for begineer as well.
Excelent platform
Databricks as a Product
Single Source for my Data Engineering work
with data Intelligence platform databricks made data pipelines and ETL process easier implementation than ever, now pipelines became more simpiler can build pipelines quick and easy.
with Data Intelligence platform delivery of project improved with frequency of pipeline builds increased.
Now its been easier to trobulshoot pipelines and spark jobs, which reduces heavy load team and customer support