Create table command is used to create a table in the already existing databaseto store data in the form of rows or columns.

In this example, I have some data into a CSV file. Spark SQL internally implements data frame API and hence, all the data sources that we learned in the earlier video, including Avro, Parquet, JDBC, and Cassandra, all of them are available to you through Spark SQL. Note that check constraints, primary keys and unique keys have been picked up along with their storage clauses. Now compare the actual table CREATE TABLE DDL generated from the new_generate_table_ddl.sql SQL*Plus script, shown next, to the earlier output from the old_generate_table_ddl.sql SQL*Plus script.This table will store records of students. We start by creating an empty table by firing the following query CREATE TABLE student (id INTEGER PRIMARY KEY, name TEXT, age INTEGER) This will create a table called “student” with 0 rows in it.An exception is file source such as Parquet, JSON. In general CREATE TABLE is creating a 'pointer', and you must make sure it points to something that exists. When you read and write table foo, you actually read and write table bar. For example, you can create a table foo in Spark that points to a table bar in MySQL using JDBC data source.This tutorial presumes the reader is familiar with using SQL with relational databases and would like to know how to use Spark SQL in Spark. We will continue to use the baby names CSV source file as used in the previous What is Spark tutorial.

Steps to get from SQL to Pandas DataFrame Step 1: Create a database In this Spark SQL tutorial, we will use Spark SQL with a CSV input data source. For illustration purposes, I created a simple database using MS Access, but the same principles would apply if you’re using other platforms, such as MySQL, SQL Server, or Oracle.

In this tutorial, I’ll show you how to get from SQL to pandas DataFrame using an example.

Loading data into table from an existing one. TRANSACTIONS_PARTITIONED (TRANSACTION_ID BIGINT, USER_ID STRING, CREDIT_NUMBER STRING, T_DATE DATE, PURCHASE_TYPE STRING, DESCRIPTION STRING) PARTITIONED BY (DATE_YEAR INT, DATE_MONTH INT) Case 2. Creating a table defining the partitioning keys, year and month.

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