Forrester recently published The Forrester Wave: Insight Platforms-as-a-Service Wave, Q3 2017. In its 36-criteria evaluation of insight platform-as-a-service (PaaS) providers, Forrester identified the eight most significant players and researched, analyzed, and scored them.
Databricks — founded by the team that started the Spark research project at UC Berkeley to help customers accelerate innovation by unifying data science, engineering and the business — was named a Strong Performer. We were excited to be amongst the highest scores for Product Vision and Performance.
Forrester defines an insight PaaS as:
An integrated set of data management, analytics, and insight application development and management components, offered as a platform the enterprise does not own or control.
In the current offering category, Forrester evaluated each vendor’s current data management, analytics, and platform management services. Given the need for collaboration across multiple stakeholders, Forrester strongly weighted vendors’ insight application development tooling as well as their platform’s ability to function as an integrated whole.
Here are some of our key takeaways from the report:
- “Databricks offers a next-generation platform for insight applications.”
The Databricks Unified Analytics Platform continues to add proprietary features through its notebooks supporting most data analytics development languages, including SQL, and a comprehensive set of machine learning and visualization libraries.
- “Databricks’ polished user interface and advanced features make it ideal for teams of citizen data scientists.”
Given our mission to accelerate innovation by reducing the time-to-insight, our powerful user interface allows data scientists of varying skills to easily leverage advanced capabilities and collaborate with each other and the business.
- “[Databricks] provides the benefits of enterprise Spark while reducing the need for complex tuning.”
Databricks, founded by the team that started the Spark research project at UC Berkeley that later became Apache Spark, continues to remove complexity for enterprises to run and manage Spark. We make it very simple for customers to select different versions of Spark for different workloads providing great migration flexibility to our customers.
We appreciate the support of all our customers, partners, and global community who have helped Databricks grow. If you’d like an expert at Databricks to walk you through our product, contact us here.