The Python support for the Couchbase Spark Connector brings first-class integration between Couchbase Server and Apache Spark to Python data engineers, enabling PySpark applications to seamlessly read from and write to Couchbase. The connector is production-ready and fully supported, allowing users to leverage Spark for ETL/ELT, real-time analytics, machine learning, and more on data stored in Couchbase. To get started with the PySpark connector, users can install PySpark using pip and include the Couchbase Spark Connector JAR in their Spark environment configuration. The connector supports reading from and writing to both Couchbase operational databases and Capella Columnar databases, allowing users to load data from a Couchbase bucket as a Spark DataFrame via SQL++ queries or use key-value operations for writes. Users can also query the columnar dataset in Couchbase using Spark SQL. To maximize throughput and efficiency when using the connector, it is recommended to use the Data service for bulk writes, increase write partitions for Query service writes, align partition counts with cluster resources, indexing smartly, and choose the right service for the job. The Couchbase PySpark support is open-source and encourages contributions, feedback, and community engagement through forums and Discord channels.