Apache Spark Python - Spark Metastore - Overview of Spark Metastore

Data Engineering Spark
1

Let us get an overview of Spark Metastore and how we can leverage it to manage databases and tables on top of Big Data based file systems such as HDFS, s3, etc. Quite often we need to deal with structured data, and the most popular way of processing structured data is by using Databases, Tables, and then SQL.

Spark Metastore (similar to Hive Metastore) will facilitate us to manage databases and tables. Typically, Metastore is set up using traditional relational database technologies such as Oracle, MySQL, Postgres, etc.

Structured Data Management

Structured data is managed using databases and tables. Let’s create a database and table in Spark Metastore using the following code snippet:

CREATE DATABASE IF NOT EXISTS myDatabase;
USE myDatabase;

CREATE TABLE IF NOT EXISTS myTable (
    id INT,
    name STRING
);

SQL Operations

SQL operations can be performed on the data stored in tables within the database. Here is an example SQL query:

SELECT * FROM myTable WHERE name = 'Alice';

Watch the video tutorial here

Hands-On Tasks

  1. Create a database in Spark Metastore.
  2. Create a table within the database.
  3. Insert some sample data into the table.
  4. Perform a SQL query to retrieve specific data.

Conclusion

In this article, we have covered the basics of Spark Metastore and how it can be utilized to manage databases and tables for structured data processing. We encourage you to practice these concepts and engage with the community for further learning.