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Title:ETL | AWS Glue | AWS S3 | Data Cleansing | Transforming data with AWS Glue in ETL workflows
Duration:24:09
Viewed:4,321
Published:19-01-2024
Source:Youtube

=================================================================== 1. SUBSCRIBE FOR MORE LEARNING : https://www.youtube.com/channel/UCv9MUffHWyo2GgLIDLVu0KQ =================================================================== 2. CLOUD QUICK LABS - CHANNEL MEMBERSHIP FOR MORE BENEFITS : https://www.youtube.com/channel/UCv9MUffHWyo2GgLIDLVu0KQ/join =================================================================== 3. BUY ME A COFFEE AS A TOKEN OF APPRECIATION : https://www.buymeacoffee.com/cloudquicklabs =================================================================== In this lab you will learn about AWS Glue, which is a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development. You can use a crawler to populate the AWS Glue Data Catalog with tables. This is the primary method used by most AWS Glue users. A crawler can crawl multiple data stores in a single run. Upon completion, the crawler creates or updates one or more tables in your Data Catalog. Extract, transform, and load (ETL) jobs that you define in AWS Glue use these Data Catalog tables as sources and targets. The ETL job reads from and writes to the data stores that are specified in the source and target Data Catalog tables. example data : https://github.com/RekhuGopal/PythonHacks/tree/main/AWS_Data_Transformation_Using_Glue #awsglue #etl #datatransformation #s3 #cloudquicklabs #crawler #etljob #dataengineering #data #datacleansing #datamodificada



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