Read data from Azure SQL and SQL Server import. You can also run a DML or DDL query in databases in SQL Database and SQL Server. You can connect to databases in SQL Database and SQL Server from a Spark job to read or write data. Download the latest versions of the JAR from the release folderĬonnect and read data using the Spark connector.To build the connector without dependencies, you can run: The following diagram illustrates the data flow.Ĭurrently, the connector project uses maven. User can choose to use row-by-row insertion or bulk insert. The Worker node connects to databases that connect to SQL Database and SQL Server and writes data to the database.

The Spark master node distributes data to worker nodes for transformation.The Spark master node connects to databases in SQL Database or SQL Server and loads data from a specific table or using a specific SQL query.The Spark connector utilizes the Microsoft JDBC Driver for SQL Server to move data between Spark worker nodes and databases: Instead, we strongly encourage you to evaluate and use the new connector. The GitHub repo for the old connector previously linked to from this page is not actively maintained. It is easy to migrate your existing Spark jobs to use this new connector. It provides similar interfaces with the built-in JDBC connector.

The Spark connector supports Azure Active Directory (Azure AD) authentication to connect to Azure SQL Database and Azure SQL Managed Instance, allowing you to connect your database from Azure Databricks using your Azure AD account. It can outperform row-by-row insertion with 10x to 20x faster performance.

Compared to the built-in JDBC connector, this connector provides the ability to bulk insert data into your database. It allows you to utilize real-time transactional data in big data analytics and persist results for ad hoc queries or reporting. The Spark connector enables databases in Azure SQL Database, Azure SQL Managed Instance, and SQL Server to act as the input data source or output data sink for Spark jobs. The information about the old connector (this page) is only retained for archival purposes. We strongly encourage you to evaluate and use the new connector instead of this one. However, Apache Spark Connector for SQL Server and Azure SQL is now available, with support for Python and R bindings, an easier-to use interface to bulk insert data, and many other improvements. As of Sep 2020, this connector is not actively maintained.

Mysql jdbc driver for spark