Custom DNS With AWS Privatelink for Databricks Workspaces

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This post was written in collaboration with Amazon Web Services (AWS). We thank co-authors Ranjit Kalidasan, senior solutions architect, and Pratik Mankad, partner solutions architect, of AWS for their contributions.   Last week, we were excited to announce the release of AWS PrivateLink for Databricks Workspaces, now in public preview, which enables new patterns and...

Private Databricks Workspaces With AWS PrivateLink Is in Public Preview

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We’re excited to announce that PrivateLink connectivity for Databricks workspaces on AWS (Amazon Web Services) is now in public preview, with full support for production deployments. This release applies to all AWS regions supporting E2 architecture, as part of the Enterprise pricing tier. We have received great feedback from our global customers, including large financial...

Azure Databricks Achieves DoD Impact Level 5 (IL5) on Microsoft Azure Government

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We are excited to announce that Azure Databricks has received a Provisional Authorization (PA) by the Defense Information Systems Agency (DISA) at Impact Level 5 (IL5), as published in the Department of Defense Cloud Computing Security Requirements Guide (DoD CC SRG). The authorization closely follows our FedRAMP High authorization and further validates Azure Databricks security...

Accenture and Databricks Partner Together to Streamline Large Scale Machine Learning Deployments

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Today, we’re excited to announce Databricks’ partnership with Accenture to provide high-value Databricks services and reusable components to enterprise clients globally. Specializing in data strategy and design, data platform modernization and AI, the Accenture data and artificial intelligence (AI) team leverages Databricks’ Unified Data Analytics Platform to streamline proven methodologies for large-scale machine learning deployments....

How to Save up to 50 Percent on Azure ETL While Improving Data Quality

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The challenges of data quality One of the most common issues our customers face is maintaining high data quality standards, especially as they rapidly increase the volume of data they process, analyze and publish. Data validation, data transformation and de-identification can be complex and time-consuming. As data volumes grow, new downstream use cases and applications...

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