Data Engineering Spark SQL - Tables - DML & Partitioning - Load vs. Insert

Let us compare and contrast LOAD and INSERT commands. These are the main approaches using which we get data into Spark Metastore tables. Let us start spark context for this Notebook so that we can execute the code provided. You can sign up for our 10 node state of the art cluster/labs to learn Spark SQL using our unique integrated LMS.

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Key Concepts Explanation

LOAD Command

LOAD will copy the files by dividing them into blocks.
LOAD is the fastest way of getting data into Spark Metastore tables. However, there will be minimal validations at the file level.
There will be no transformations or validations at the data level.

INSERT Command

If it requires any transformation while getting data into Spark Metastore table, then we need to use the INSERT command.
Here are some of the usage scenarios of INSERT:

  • Changing delimiters in case of a text file format
  • Changing file format
  • Loading data into partitioned or bucketed tables (if bucketing is supported)
  • Apply any other transformations at the data level (widely used)

Hands-On Tasks

Description of the hands-on tasks. Provide a list of tasks that the reader can perform to apply the concepts discussed in the article.

  1. Execute the provided code to create a table in Spark Metastore
  2. Load data from a local path into the created table using the LOAD command
  3. Run a query to display the first 10 rows of the loaded data


In this article, we discussed the differences between LOAD and INSERT commands in Spark Metastore tables. While LOAD is fast and minimal, INSERT allows for transformations and validations at the data level. Practice these concepts by performing the hands-on tasks and engage with the community for further learning.

Remember, learning by doing is the best way to master these concepts.

Watch the video tutorial here