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ETL (Extract, Transform, Load) pipelines are an essential part of the data management process. They involve extracting data from various sources, cleaning and restructuring it, before loading it into a data warehouse. Databricks, an open-source data analytics platform, provides a unified and convenient environment for running these ETL operations. This blog post will guide you through the process of building SQL ETL pipelines in Databricks step by step.
Creating a Databricks Cluster
First and foremost, you need a Databricks cluster to run the jobs. Depending on your requirements, you can choose the size and type of the cluster you need. Generally, you should go for high-memory machines to perform heavy transformations.
Getting the Data Source
Most often, your data will reside in a traditional data warehouse such as Amazon Redshift, Google BigQuery, or perhaps in an HDFS (Hadoop Distributed File System). Connect to your source using the appropriate connector available in Databricks. It can be as simple as adding a few lines of SQL like the following:
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CREATE TEMPORARY TABLE emp_load USING com.databricks.spark.redshift OPTIONS ( url 'jdbc:redshift://<your-cluster-url>:5439/database?user=username&password=pass', dbtable 'public.emp', tempdir 's3://bucket/temp-dir/' ) |
Extracting and Transforming
Once the table is available as a temporary table in the Spark context, you can perform various operations such as filtering, aggregation, joining, etc. Let’s have an example of extracting the first name and last name of the employees and creating a new field, fullname:
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SELECT first_name, last_name, first_name || ' ' || last_name AS fullname FROM emp_load |
Loading Data Back to the Data Warehouse
After processing, the final step of ETL is loading the transformed data back to the data warehouse. It can be done with a simple SQL statement:
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CREATE TABLE emp_transformed USING com.databricks.spark.redshift OPTIONS ( url 'jdbc:redshift://<your-cluster-url>:5439/database?user=username&password=pass', dbtable 'public.emp_transformed', tempdir 's3://bucket/temp-dir/' ) AS SELECT * FROM emp_tranformed_temp |
Conclusion
Building an ETL pipeline using SQL in Databricks isn’t as intimidating as it sounds. With just a few statements, you can extract, transform, and load your data.