s/HdfsDesign.html], with permissions. Ease of Use You only need to be familiar with Structured Query Language (SQL) and Java Database Connectivity (JDBC) to handle massive data. It helps you focus on Extensions Connectors Connectors Connectors for Spark SQL Query Engine Connectors For Flink SQL Query Engine Connectors for Hive SQL Query Engine Connectors For Trino SQL Engine Kyuubi Insider Overview Execute Spark SQL Statements If the beeline session is successfully connected, then you can run any query supported by Spark SQL now. For example, 0: jdbc:hive2://10.242.189.214:2181/> select timestamp '2018-11-17';

s/HdfsDesign.html], with permissions. Ease of Use You only need to be familiar with Structured Query Language (SQL) and Java Database Connectivity (JDBC) to handle massive data. It helps you focus on Extensions Connectors Connectors Connectors for Spark SQL Query Engine Connectors For Flink SQL Query Engine Connectors for Hive SQL Query Engine Connectors For Trino SQL Engine Kyuubi Insider Overview Execute Spark SQL Statements If the beeline session is successfully connected, then you can run any query supported by Spark SQL now. For example, 0: jdbc:hive2://10.242.189.214:2181/> select timestamp '2018-11-17';

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.0 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.3 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.3 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.0 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.0 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.2 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.2 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation

houses. Kyuubi builds 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 miscellaneous ones. – It provides methods that allow clients to submit SQL queries and receive the query results, submit metadata requests and receive metadata results. – It enables easy submission of self-contained Release 1.7.1 2.3 High Performance Query performance is one of the critical factors in implementing Serverless SQL. Implementing serviceability on state- of-the-art query engines for bigdata lays the foundation