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.
Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.
Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.
Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.
Databricks Key Features
Some of Databricks key features include:
Reviews from Real Users
Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.
PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”
A business intelligence coordinator in construction notes, “The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes.”
An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”
Innovation to break decades of data storage trade-offs.
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