{"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":"
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