For an instructor lead, in-depth look at learning SQL click below.
Databricks SQL automation is a powerful tool used to create scheduled jobs and workflows. It allows data devotees working with the Databricks Unified Analytics Platform to automate SQL-based workflows, saving time and improving consistency. Using it, tasks such as data clean-up, analysis, and reporting can be scheduled to execute automatically.
Databricks and SQL
Databricks allows users to perform advanced data analytics efficiently. In combination with SQL, an industry-standard language for data manipulation, it provides a powerful platform for data management. SQL code can be used to automate processes in Databricks, freeing up resources and improving accuracy in data processing.
Creating a Scheduled SQL Job
Let’s start with the basics by scheduling a simple SQL job. The following script selects all records from a specified table.
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CREATE DATABASE IF NOT EXISTS TestDB; USE TestDB; CREATE TABLE IF NOT EXISTS TestTable; --insert some data INSERT INTO TestTable VALUES ('1', 'FirstValue'), ('2', 'SecondValue'); --View the table SELECT * FROM TestTable; |
You could then use Databricks’ schedule function to run this script at your desired frequency.
Building a SQL Workflow
A more complex example is creating a SQL workflow consisting of multiple jobs. In these cases, the output of one SQL statement forms the input for the next. See the code below:
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-- Create a second table CREATE TABLE IF NOT EXISTS SecondTable; -- Insert data into the second table only if the first entry in TestTable is '1' INSERT INTO SecondTable SELECT * FROM TestTable WHERE column1 = '1'; |
Using SQL automation in Databricks, you’d oversight this flow, ensuring that the jobs run in a correct order and data integrity is maintained.
Testing the Scheduled Jobs and Workflow
After creating the schedule, it’s necessary to validate that they are set up correctly. Databricks provides the capability to test the SQL jobs manually before activating the schedule. Specific to SQL workflows, this step is crucial to ensure that all dependencies are handled correctly, data is processed in the correct sequence, and that the total process time meets your expectations.
With the basics outlined here, and with further learning and experience, you can start using Databricks SQL automation to offload routine activities and focus more on strategic tasks – truly leveraging the power and convenience of modern data analytics platforms!