{"id":1786301,"date":"2022-12-26T14:50:50","date_gmt":"2022-12-26T19:50:50","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/?p=100235"},"modified":"2022-12-26T14:50:50","modified_gmt":"2022-12-26T19:50:50","slug":"crafting-serverless-etl-pipeline-using-aws-glue-and-pyspark","status":"publish","type":"station","link":"https:\/\/platodata.io\/plato-data\/crafting-serverless-etl-pipeline-using-aws-glue-and-pyspark\/","title":{"rendered":"Crafting Serverless ETL Pipeline Using AWS Glue and PySpark"},"content":{"rendered":"
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ETL (Extract, Transform, and Load) is a very common technique in data engineering. It involves extracting the operational data from various sources, transforming it into a format suitable for business needs, and loading it into data storage systems.<\/p>\n

Traditionally, ETL processes are run on servers, which ongoing maintenance and manual intervention. However, with the rise of serverless technology, it is now possible to perform ETL without the need for dedicated servers. This is where AWS Glue and PySpark come into play.<\/p>\n

AWS Glue is a fully managed ETL offering from AWS that makes it easy to manipulate and move data between various data stores. It can crawl data sources, identify data types and formats, and suggest schemas, making it easy to extract, transform, and load data for analytics.<\/p>\n

PySpark<\/a> is the Python wrapper of Apache Spark (which is a powerful open-source distributed computing framework widely used for big data processing).<\/p>\n

How Do AWS Glue and PySpark Work?<\/h2>\n<\/div>\n
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Together, Glue and PySpark provide a powerful, serverless ETL solution that is easy to use and scalable. Here\u2019s how it works:<\/p>\n

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  1. First, Glue crawls your data sources to identify the data formats and suggest a schema. You can then edit and refine the schema as needed.<\/li>\n
  2. Next, you use PySpark to write ETL scripts that extract the data from the sources, transform it according to the schema, and load it into your data warehouse or other storage systems.<\/li>\n
  3. The PySpark scripts are then executed by Glue, which automatically scales up or down to handle the workload. This allows you to process large amounts of data without having to worry about managing servers or infrastructure.<\/li>\n
  4. Finally, Glue also provides a rich set of tools for monitoring and managing your ETL processes, including a visual workflow editor, job scheduling, and data lineage tracking.<\/li>\n<\/ol>\n

    The Usecase<\/h2>\n<\/div>\n
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    In this use case, we will develop a sample data pipeline (Glue Job) using the AWS typescript SDK, which will read the data from a dynamo DB table, perform some data transformation using PySpark and write it into an S3 bucket in CSV format. DynamoDB is a fully managed NoSQL database service offered by AWS, which is easily scalable and used in multiple applications. On the other hand, S3 is a general-purpose storage offering by AWS.<\/p>\n

    For simplicity, we can consider this as a use case for moving an application or transactional data to the data lake.<\/p>\n

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    \"AWS<\/div>\n<\/div>\n<\/figure>\n

    Project Structure<\/h2>\n<\/div>\n
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