multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server]

multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server]

multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server]

multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server]

multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the- thrift-jdbcodbc-server]

multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the- thrift-jdbcodbc-server]

multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the- thrift-jdbcodbc-server]

distributed SQL query engines on top of various kinds of modern computing frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive combine some of the components above to build a modern data stack. For example, you can use Kyuubi, Spark and Iceberg [https://iceberg.apache.org/] to build and manage Data Lakehouse with pure SQL for both results. It enables easy submission of self-contained applications for batch processing, such as Spark jobs. MySQL Protocol A MySQL-compatible interface that allows end users to use MySQL Connectors

distributed SQL query engines on top of various kinds of modern computing frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive combine some of the components above to build a modern data stack. For example, you can use Kyuubi, Spark and Iceberg [https://iceberg.apache.org/] to build and manage Data Lakehouse with pure SQL for both results. It enables easy submission of self-contained applications for batch processing, such as Spark jobs. MySQL Protocol A MySQL-compatible interface that allows end users to use MySQL Connectors

distributed SQL query engines on top of various kinds of modern computing frameworks, e.g., Apache Spark, Flink, Doris, Hive, and Trino, etc, to query massive datasets distributed over fleets of machines combine some of the components above to build a modern data stack. For example, you can use Kyuubi, Spark and Iceberg to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and results. – It enables easy submission of self-contained applications for batch processing, such as Spark jobs. • MySQL Protocol – A MySQL-compatible interface that allows end users to use MySQL Connectors