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To begin, create an IAM role for AWS Glue with the necessary permissions. Ensure this role has access to both DynamoDB and S3. Attach the `AmazonDynamoDBReadOnlyAccess` and `AmazonS3FullAccess` policies to the role. Additionally, attach the `AWSGlueServiceRole` policy to allow AWS Glue to perform operations on your behalf.
Navigate to the AWS Glue Console and create a new crawler. Set the data source to DynamoDB and specify the table you want to export. Configure the crawler to create a table in the Glue Data Catalog, which describes the structure of your DynamoDB data. Run the crawler to populate the Data Catalog with metadata about your DynamoDB table.
Set up an S3 bucket where your data will be exported. Ensure that the bucket has the appropriate permissions to allow AWS Glue to write data to it. You can set up access policies that restrict access to authorized users and services only.
In the AWS Glue Console, create a new ETL job. Select the IAM role created in Step 1. Choose the Data Catalog table generated by the crawler as the data source. For the target, specify the S3 bucket and the desired format (e.g., CSV, JSON, Parquet). Configure the job script to transform and load the data from DynamoDB to S3.
If your data requires transformation, modify the automatically generated PySpark script. Use AWS Glue�s built-in transformations, such as mapping, filtering, and joining, to process your data as needed. Save and test the script to ensure it behaves as expected with sample data.
Execute the ETL job by clicking the "Run Job" button in the AWS Glue Console. Monitor the job progress and check for any errors or warnings in the AWS Glue Job Run details. Use CloudWatch logs to debug issues if necessary. The job will read data from DynamoDB, transform it, and write the output to the specified S3 bucket.
After the job completes successfully, navigate to the S3 console and verify that the data is present in the specified bucket and in the correct format. Use Amazon Athena to query and validate the data structure and contents, ensuring the transformation process preserved data integrity.
By following these steps, you can effectively transfer data from DynamoDB to S3 using AWS Glue without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
