Databricks FAQ

Anyone who wants to extract value from their big data quickly and efficiently ranging from data scientists and engineers to developers and data analysts. By providing an interactive workspace the exposes Spark’s native R, Scala, Python and SQL interfaces; a REST API for remote programmatic access; the ability to execute arbitrary Spark jobs developed offline; and seamless support for 3rd party applications such as BI and domain-specific tools; Databricks enables users to consume data and insights through the interface they’re most comfortable with.

Databricks is being used by enterprises from a wide variety of verticals, including financial services, healthcare, retail, media & entertainment, and utilities. To date, we have seen customers utilize our platform for a broad spectrum of use cases including core ETL, data discovery and exploration, data warehousing, data product deployment, and insight publishing using dashboards for internal and external audiences.

Absolutely. Enterprises are accumulating massive quantities of data, but the big data analysis process in itself brings many barriers, ranging from infrastructure management needs to provisioning bottlenecks to high costs of acquisition and management. Databricks is designed to remove all these hurdles. We want big data to become as easy to use for the enterprises, making it as common as business applications used today like Excel.

Databricks was founded by the team who started the Spark research project at UC Berkeley, which later became Apache Spark™. Databricks works with the open source community to continue to expand the project. We have contributed more code to Spark than any other company. We also provide Databricks certification programs for Spark developers, system integrators, applications, distributors, and trainers. Additionally, we’ve developed Databricks, a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.