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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 1.5.2. Execute Statements If the beeline session is successfully connected, then you can run any query supported by Spark SQL now. For example, 0: jdbc:hive2://localhost:10009/> 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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 1.5.2. Execute Statements If the beeline session is successfully connected, then you can run any query supported by Spark SQL now. For example, 0: jdbc:hive2://localhost:10009/> 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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 1.5.2. Execute 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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 1.5.2. Execute 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';

Chapter 1. Multi-tenancy CHAPTER TWO 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 analytics engine. • Concurrent execution: multiple Spark applications work together • Quick response: long-running Spark applications without startup cost • Optimal execution plan: fully supports Spark SQL SparkSubmit Execute Statements If the beeline session is successfully connected, then you can run any query supported by Spark SQL now. For example, 0: jdbc:hive2://localhost:10009/> select timestamp '2018-11-17';

Chapter 1. Multi-tenancy CHAPTER TWO 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 analytics engine. • Concurrent execution: multiple Spark applications work together • Quick response: long-running Spark applications without startup cost • Optimal execution plan: fully supports Spark SQL SparkSubmit Execute Statements If the beeline session is successfully connected, then you can run any query supported by Spark SQL now. For example, 0: jdbc:hive2://localhost:10009/> select timestamp '2018-11-17';

hdfs/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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 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';

hdfs/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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 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';

hdfs/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 analytics engine. Concurrent execution: multiple Spark applications work together Quick response: long-running Spark applications without startup cost Optimal execution plan: fully supports Spark SQL 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';

Chapter 1. Multi-tenancy CHAPTER TWO 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 analytics engine. • Concurrent execution: multiple Spark applications work together • Quick response: long-running Spark applications without startup cost • Optimal execution plan: fully supports Spark SQL SparkSubmit Execute 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';