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 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 data processing e.g. ETL 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 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 data processing e.g. ETL 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]

Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], Trino [https://trino.io/], and StarRocks [https://www.starrocks 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 data processing, HiveServer2-compatible interface that allows end users to use a thrift client(cross-language support, both tcp and http), a Java Database Connectivity(JDBC) interface over thrift, or an Open Database Connectivity (ODBC)

frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino.io/], etc, to query 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 data processing, HiveServer2-compatible interface that allows end users to use a thrift client(cross-language support, both tcp and http), a Java Database Connectivity(JDBC) interface over thrift, or an Open Database Connectivity (ODBC)

frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino.io/], etc, to query 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 data processing, HiveServer2-compatible interface that allows end users to use a thrift client(cross-language support, both tcp and http), a Java Database Connectivity(JDBC) interface over thrift, or an Open Database Connectivity (ODBC)

frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino.io/], etc, to query 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 data processing, HiveServer2-compatible interface that allows end users to use a thrift client(cross-language support, both tcp and http), a Java Database Connectivity(JDBC) interface over thrift, or an Open Database Connectivity (ODBC)

frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino.io/], etc, to query 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 data processing, HiveServer2-compatible interface that allows end users to use a thrift client(cross-language support, both tcp and http), a Java Database Connectivity(JDBC) interface over thrift, or an Open Database Connectivity (ODBC)

frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino.io/], etc, to query 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 data processing, HiveServer2-compatible interface that allows end users to use a thrift client(cross-language support, both tcp and http), a Java Database Connectivity(JDBC) interface over thrift, or an Open Database Connectivity (ODBC)

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 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 data processing e.g. ETL 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 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 data processing e.g. ETL 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]