Project Overview This project demonstrates a serverless ETL pipeline using AWS Glue to transform and load data from Amazon S3 into Amazon Redshift. The project leverages AWS Glue's ETL capabilities, Glue Crawlers, and the AWS Glue Data Catalog to automate the data transformation process, making it suitable for scalable data processing and integration tasks.

Architecture The ETL pipeline follows these steps:

Data Ingestion: Raw data is ingested from the source and uploaded to an Amazon S3 bucket.

Data Discovery with AWS Glue Crawler (Source Data): An AWS Glue Crawler is configured to scan the data in the S3 bucket. The crawler extracts the schema and metadata, which is stored in the AWS Glue Data Catalog.

Data Transformation with AWS Glue ETL: AWS Glue ETL jobs are created to transform the data according to the specified business rules. The ETL job reads data from the S3 bucket, applies transformations, and prepares it for loading.

Data Loading to Amazon Redshift: The transformed data is loaded into a target table in Amazon Redshift. Redshift serves as the destination for the processed data, enabling efficient querying and analytics.

Data Cataloging with AWS Glue Crawler (Target Table): An additional AWS Glue Crawler is created to catalog the target table in Amazon Redshift. This crawler updates the AWS Glue Data Catalog with the schema of the transformed data, enabling easier data discovery and integration with other tools.

Key Components Amazon S3: Acts as the staging area for raw data. AWS Glue Crawler: The first crawler scans and catalogs the source data in S3. The second crawler catalogs the target table in Redshift for easy discovery and querying. AWS Glue Data Catalog: Stores metadata information, allowing easy access to structured data for transformation and analysis. AWS Glue ETL Job: Performs data transformation by reading from S3, applying transformations, and loading the result into Amazon Redshift. Amazon Redshift: Stores the final transformed data for querying and analysis.

Setup Instructions Create an S3 Bucket: Upload the raw data to an Amazon S3 bucket. Configure AWS Glue Crawler (Source Data): Set up a crawler to catalog the source data in S3 and store the schema in the AWS Glue Data Catalog. Set up Amazon Redshift: Create a database and schema in Redshift. Create the target table in Redshift for storing transformed data. Run AWS Glue ETL Job: Set up an ETL job in AWS Glue to read data from S3, apply transformations, and load it into Redshift. Configure AWS Glue Crawler (Target Table): Create a second crawler to catalog the target table in Amazon Redshift. This step updates the Glue Data Catalog with the final schema of the transformed data. Monitor the Job: Monitor the ETL job in the AWS Glue Console to ensure successful completion.

Prerequisites AWS account with access to Amazon S3, AWS Glue, and Amazon Redshift. IAM roles configured with the necessary permissions for AWS Glue and Amazon Redshift. Usage Upload raw data to the specified S3 bucket. Run the AWS Glue Crawler to catalog the source data. Execute the AWS Glue ETL job to transform and load data into Redshift. Run the second Glue Crawler to catalog the target table in Redshift. Access the transformed data in Amazon Redshift for analysis and reporting.