SAN FRANCISCO – March 17, 2020 – Databricks, the leader in unified data analytics, today announced new features within its platform that provide deeper security controls, proactive administration and automation across the data and ML lifecycle.
As data teams enable analytics and machine learning (ML) applications across their organizations, they require the ability to securely leverage data at massive scale. Doing this can be complex and risky, especially when operating in a multi-cloud environment. Security is fragmented, which makes corporate access policies difficult to extend, administration is reactive and inefficient, and devops processes like user management or cluster provisioning are manual and time consuming. Databricks’ Unified Data Analytics Platform addresses these challenges by helping organizations bring all their users and data together in a simple, scalable and secure service that can leverage the native capabilities of multiple clouds.
“The biggest challenge for organizations today is selecting an enterprise platform that can handle all of your data and all of the people that interact with it – today and in the future,” said David Meyer, senior vice president of Product Management at Databricks. “Databricks is the only platform that has successfully achieved the massive scale and simplicity that enables enterprises to make data, business analytics and machine learning pervasive enterprise-wide. We’re committed to preserving this for our customers, regardless of if and how their cloud strategies evolve over time. These new features are a great example of how we’re doing that.”
New features within the Databricks platform further enhance:
- Cloud-native Security – Enterprises can already leverage a fully-managed SaaS service without losing control over their data by running Databricks clusters inside their cloud account. The addition of customer-owned revocable data encryption keys and customized private networks to run these clusters, allows customers to further tailor the service to their unique enterprise and compliance requirements.
- Simple and Proactive Administration – To support hundreds of teams with thousands of users that create hundreds of thousands of compute instances, visibility and control are critical. For full transparency organizations can now audit and analyze all the activity in their account, and set policies to administer users, control budget and manage infrastructure.
- Automation at Scale – With an API-driven approach, Databricks now enables customers to productionize analytics and ML rapidly with CI/CD (Continuous integration and continuous delivery). With the addition of git support, APIs for everything from user management, workspace provisioning, cluster policies to application and infrastructure monitoring, DevOps teams can automate the whole data and ML lifecycle.
Webinar: Simplify, Secure and Scale your Enterprise Cloud Data Platform on Databricks
Blog: Enabling Massive Data Transformation Across Your Organization
Blog: Security that unblocks the true potential of your data lake
Blog: Delivering and Managing a Cloud-Scale Enterprise Data Platform with Ease
Blog: Productionize and automate your data platform at scale
Databricks helps data teams solve the world’s toughest problems. As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. The company’s global customer base has thousands of organizations including Comcast, Shell, Expedia, and Regeneron. Databricks is venture-backed and founded by the original creators of popular open source projects, including Apache Spark, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Apache, Apache Spark and Spark are trademarks of the Apache Software Foundation.
Head of Communications