We compared Databricks and VAST Data based on our users reviews in five parameters. After reading the collected data, you can find our conclusion below:
Comparison Results: Databricks is known for its complexity during initial setup, especially regarding database and third-party components. VAST Data stands out for its simple and efficient setup, which can be completed in less than a day. Regarding features, Databricks offers a comprehensive range of functionalities such as stream events, automated cluster creation, and universal data access. It is also commendable in managing large datasets and provides language flexibility. On the other hand, VAST Data excels in failover capability, resiliency, and encryption. Opinions on pricing for Databricks vary, with some considering it expensive while others find it reasonably priced. VAST Data falls in the middle category in terms of pricing, setup cost, and licensing. Customer service and support for both platforms have generally positive feedback. Databricks provides good technical support, and VAST Data is highly regarded for its prompt and efficient assistance with quick response times. To summarize, Databricks offers a wide range of functionalities and flexibility, while VAST Data is valued for its simplicity, efficiency, and failover capability.
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"Databricks helps crunch petabytes of data in a very short period of time."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"The processing capacity is tremendous in the database."
"It's very simple to use Databricks Apache Spark."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"The technical support is good."
"This has been one of the most reliable storage systems that I have ever used."
"The solution is useful for machine learning and scientific applications, including computer simulations."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"I would like it if Databricks made it easier to set up a project."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"The product should provide more advanced features in future releases."
"Implementation of Databricks is still very code heavy."
"It would be great if Databricks could integrate all the cloud platforms."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"The read/write ratio is an area in the solution with some flaws and needs improvement."
"The write performance could be improved because it is less than half of the read performance."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while VAST Data is ranked 8th in NVMe All-Flash Storage Arrays with 2 reviews. Databricks is rated 8.2, while VAST Data is rated 10.0. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of VAST Data writes "Stability-wise, a device that has been up and running for years". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas VAST Data is most compared with Pure Storage FlashBlade, NetApp AFF, Pure Storage FlashArray, Qumulo and Red Hat Ceph Storage.
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