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Deepak Goyal - Test Shell
In this lab, you will build a simple data ingestion and preparation workflow using AWS Glue. You will begin by downloading a sample CSV dataset and uploading it to an Amazon S3 source bucket. Next, you will use an AWS Glue Crawler to discover and catalog the data, create an AWS Glue ETL job to transform and prepare the dataset, and store the processed output in a destination Amazon S3 bucket. By the end of the lab, you will have practical experience with data ingestion, schema discovery, catalog management, and ETL processing using AWS Glue.
Lab Info
Table of Contents
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Challenge
Upload source data to Amazon S3
Download the provided https://github.com/pluralsight-cloud/Lab-Ingesting-Data-Using-AWS-Glue/blob/main/order.csv dataset and upload it to the pre-created Amazon S3 source bucket. Verify that the file has been uploaded successfully and is available for processing by AWS Glue.
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Challenge
Use an AWS Glue crawler to catalog the dataset
Create and configure an AWS Glue Crawler to scan the uploaded dataset. Run the crawler and register the discovered schema in the AWS Glue Data Catalog. Verify that the database and table definitions were created successfully.
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Challenge
Create and configure an AWS Glue ETL job
Create an AWS Glue ETL job using the cataloged dataset as the source. Configure the job to perform simple data preparation tasks such as selecting columns, renaming fields, and filtering records into a refined dataset. Configure the ETL job to write the transformed output to the destination Amazon S3 bucket.
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Challenge
Run and validate the processed dataset
Run the job, monitor its execution, and verify that the processed dataset has been successfully generated and stored in the target location.
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