![]() ![]() ![]() Included in this GitHub repository are a number of sample notebooks and scripts that you can utilize: spark-shell, pyspark, spark-submit), you can use the -packages parameter with the connector's maven coordinates. To work with the connector using the spark-cli (i.e. Instead of downloading the six separate jars into six different libraries, you can download the uber jar from maven at ) and install this one jar/library. Note, the Databricks documentation at is not up to date. Please create a library using within your Databricks workspace by following the guidance within the Azure Databricks Guide > Use the Azure Cosmos DB Spark connector Review the connector's maven versions Spark You can build and/or use the maven coordinates to work with azure-cosmosdb-spark. ![]() Review supported component versions Component Upsert the dataframe to Cosmos DB import org. "PreferredRegions " - > "Central US East US2 ", Configure connection to the sink collection val writeConfig = Config( Map( Table of Contentsīelow are excerpts in Python and Scala on how to create a Spark DataFrame to read from Cosmos DB It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in python and scala. The quick start introduction: Config Reference: End-to-end samples: - Azure Cosmos DB Connector for Apache SparkĪzure-cosmosdb-spark is the official connector for Azure CosmosDB and Apache Spark. The source code for the new connector is located here: Ī migration guide to change applications which used the Spark 2.4 connector is located here: The Maven coordinates (which can be used to install the connector in Databricks) are " :azure-cosmos-spark_3-1_2-12:4.0.0" The new Cosmos DB Spark connector has been released. NOTE: There is a new Cosmos DB Spark Connector for Spark 3 available. ![]()
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