Data Engineering Spark SQL - Tables - DML & Partitioning - Introduction to Partitioning


In this article, we will explore the concept of partitioning in Spark Metastore tables. We will learn about how partitioning works and its benefits in managing data efficiently.

Explanation for the video

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Key Concepts Explanation

Key Concept 1

Partitioning in Spark Metastore tables helps organize data based on a specific column value. Here is an example of how to create a partitioned table in Spark Metastore:

CREATE TABLE table_name (
    column1 STRING,
    column2 INT
PARTITIONED BY (partition_column STRING);

Key Concept 2

Static partitions can be added to a partitioned table and data can be inserted as follows:

ALTER TABLE table_name ADD PARTITION (partition_column='value');
INSERT INTO table_name PARTITION (partition_column='value') VALUES (...);

Hands-On Tasks

  1. Create a partitioned table in Spark Metastore.
  2. Add static partitions to the table.
  3. Insert data into the partitioned table.


In conclusion, we have learned about the concept of partitioning in Spark Metastore tables and how it helps in efficiently managing data. Practice creating partitioned tables and adding partitions to enhance your understanding. Join the community for further learning and discussions.

Watch the video tutorial here