March 22, 2018

Microsoft Announces GA of Azure Databricks

March 22, 2018 — Microsoft has announced the general availability of Azure Databricks, a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure.

Azure Databricks is designed in collaboration with Databricks whose founders started the Spark research project at UC Berkeley, which later became Apache Spark. Microsoft’s goal with Azure Databricks is to help customers accelerate innovation and simplify the process of building Big Data & AI solutions by combining the best of Databricks and Azure.

To meet this goal, Microsoft developed Azure Databricks with three design principles.

First, enhance user productivity in developing Big Data applications and analytics pipelines. Azure Databricks’ interactive notebooks enable data science teams to collaborate using popular languages such as R, Python, Scala, and SQL and create powerful machine learning models by working on all their data, not just a sample data set. Native integration with Azure services further simplifies the creation of end-to-end solutions. These capabilities have enabled companies such as renewables and AI to boost the productivity of their data science teams by over 50 percent.

Second, enable Microsoft’s customers to scale globally without limits by working on big data with a fully managed, cloud-native service that automatically scales to meet their needs, without high cost or complexity. Azure Databricks not only provides an optimized Spark platform, which is much faster than vanilla Spark, but it also simplifies the process of building batch and streaming data pipelines and deploying machine learning models at scale. This makes the analytics process faster for customers such as E.ON and Lennox International enabling them to accelerate innovation.

Third, ensure that Microsoft provides its customers with the enterprise security and compliance they have come to expect from Azure. Azure Databricks protects customer data with enterprise-grade SLAs, simplified security and identity, and role-based access controls with Azure Active Directory integration. As a result, organizations can safeguard their data without compromising productivity of their users.

High-performance connectivity to Azure SQL Data Warehouse, a petabyte scale, and elastic cloud data warehouse allows organizations to build Modern Data Warehouses to load and process any type of data at scale for enterprise reporting and visualization with Power BI. It also enables data science teams working in Azure Databricks notebooks to easily access high-value data from the warehouse to develop models.

Integration with Azure IoT HubAzure Event Hubs, and Azure HDInsight Kafka clusters enables enterprises to build scalable streaming solutions for real-time analytics scenarios such as recommendation engines, fraud detection, predictive maintenance, and many others.

Integration with Azure Blob StorageAzure Data Lake StoreAzure SQL Data Warehouse, and Azure Cosmos DB allows organizations to use Azure Databricks to clean, join, and aggregate data no matter where it sits.


Source: Microsoft

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