Databricks, the company founded by the creators of Apache Spark, launched major enhancements to its cloud-based platform with the 2.0 release. The new features securely manage data access for large teams while streamlining Spark application development and deployment for today’s enterprises facing complex and fast-paced environments. This milestone comes six weeks after the company announced the General Availability of the platform at Spark Summit in San Francisco. Since its founding, Databricks has attracted several thousand signups with numerous enterprise-grade deployments at organizations such as Edmunds and MyFitnessPal.
Since the General Availability announcement, we’ve been adding new capabilities at lightning speed to improve usability and security for our enterprise customers,” said Ali Ghodsi, co-founder and vice president of Engineering and Product Management at Databricks. “Utilizing the power of Databricks and Spark communities, these enhancements represent a large leap forward in our product roadmap, making Databricks the first platform to provide these innovations for enterprises.”
The Databricks 2.0 platform will eliminate the need for data professionals to contend with operational complexities presented by the wide range of tools and systems in traditional data solutions. Version 2.0 of Databricks is the first data platform in the industry to offer these capabilities for Spark:
- Access Control – Further improving security and manageability for large teams with diverse roles and responsibilities — users will now be able grant and restrict access of code and data on an individual basis with flexible Access Control.
- R Language Support– Enabling a new category of users to take advantage of Apache Spark — users will now explore data at scale with R, including one-click visualizations and instant deployment of R code into production.
- Multiple Spark Versions – Accommodating diverse production environments — users can now maintain compatibility while simultaneously experimenting with the latest features by deploying multiple versions of Apache Spark within the Databricks platform.
- Notebook Versioning – Comprehensive support for sophisticated code development processes — users can now manage and track the evolution of the codebase by integrating with popular version control tools, such as GitHub.
Managing access to credentials and other sensitive information for every user on my team has been a big challenge,” said Benny Blum, VP of Product & Data Science at Sellpoints. “The ability to quickly and easily do so with the Databricks Access Control feature will enable my team to maintain the highest security standard effortlessly, and ultimately allow them to work with critical data in a more productive manner.”
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