Why Switching Career from Java to Hadoop Read Full Explained Article From Here
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Meaning of Spark SQL: Spark SQL is programming module for working with structured data using data frame and data set abstractions. Spark SQL is the good optimization technique. In Spark SQL we can be querying the data from Spark inside that connect through JDBC and ODBC connectors to Spark SQL. Spark SQL act as a SQL query engine. Features of Spark SQL: Integrated – Spark SQL is the mixes of SQL queries so we can run queries complex analytic programs using tight integration property of Spark SQL. Unified Data Access – In Spark SQL we can load and be querying the data from various resources. Standard Connectivity – Spark SQL include server mode with standard JDBC and ODBC connectors. Scalability – In Spark SQL we can use one engine for interactive and long queries. Spark SQL Data Frames: Data Frame is the collections of distributed collections of data which organized into named columns. Data Frames is equivalent to relational tables or R/Python and it constr
Apache Hive Data Types Hive is Data warehousing tool and used to process the data stored in hadoop and HDFS. Hive is similar to SQL because it analyze and process the data through querying language. In this article we are discuss about basic data types for Hive query processing. Hive Data Types are classified into four types, given as follows Column Types Literal Null Values Complex Types Column Types: 1. Integral Integer type data can be used to Integral data types. Integral data types mentioned as INT. There are four types INT data types TINYINT (1-byte signed integer, from -128 to 127) SMALLINT (2-byte signed integer, from -32,768 to 32,767) INT (4-byte signed integer, from -2,147,483,648 to 2,147,483,647) BIGINT (8-byte signed integer, from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807) 2. String Types: String Data Types mentioned by Single Quotes and Double Quotes. It contains two types – CHAR, VARCHAR CHAR – CHAR is the fixed leng