Accelerate big data analytics with the Spark 3.0 connector for SQL Server—now generally available


Written by Daniel Coelho, Senior Program Manager


This blog post has been co-authored by Bhanu Prakash, Principal Program Manager, Azure Databricks.


We are now announcing the general availability of the Apache Spark 3.0 compatible Apache Spark Connector for SQL Server and Azure SQL, accessible through Maven.


The Spark 3.0 compatible connector went into preview early this year. Since then, we have seen tremendous customer adoption and received helpful customer feedback. Over the last few months, after incorporating enhancements and bug fixes to the connector, we are now excited about the general availability of this connector so that customers can expand their usage for even more workloads.


The Apache Spark Connector for SQL Server is a high-performance connector that enables users to use transactional data in big data analytics and persist results for ad-hoc queries or reporting. It allows you to use SQL Server or Azure SQL as input data sources or output data sinks for Spark jobs. It provides bulk insert data into the database and can outperform row-by-row insertion with 10 to 20 times faster performance, as compared to just using Java Database Connectivity (JDBC). In addition, customers can use this connector to score machine learning models from SQL Server Machine Learning Services, or score results in SQL after doing machine learning in Spark.


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