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Curating raw data in S3 to production ready data

Building Data Lakes in AWS with S3, Lambda, Glue, and Athena from ...


Curating raw data stored in one Amazon S3 bucket into curated data in another S3 bucket typically involves a series of data processing and transformation steps. You can use various AWS services and tools to automate this curation process. Here is a general guide on how you can curate your raw S3 data into a curated format:

  1. Define Your Data Processing Workflow:
  • Clearly define the steps involved in curating your raw data. This may include data cleaning, transformation, enrichment, and other processing steps based on your specific use case and business requirements.
  1. AWS Glue:
  • Use AWS Glue, a fully managed extract, transform, and load (ETL) service, to create a Glue Data Catalog that defines the structure of your raw data. This catalog will help automate schema discovery and mapping.


    • Set up a Glue Crawler to discover the schema of your raw data stored in the raw S3 bucket.
    • Define and execute Glue ETL jobs to transform the data based on your curated data model.
    • Output the curated data to a new S3 bucket.
  1. AWS Athena:
  • Use Amazon Athena, a serverless query service, to query the curated data stored in the new S3 bucket using standard SQL queries.


    • Create a table in Athena that references the curated data in the new S3 bucket.
    • Run SQL queries to analyze and explore the curated data.
  1. AWS Step Functions:
  • Use AWS Step Functions to orchestrate and automate the entire data curation workflow. Step Functions can coordinate the execution of multiple AWS services in a serverless manner.


    • Create a Step Functions state machine that defines the sequence of tasks for data curation.
    • Use Lambda functions or Glue jobs as individual steps in your state machine.
  1. AWS Lambda:
  • Use AWS Lambda to run serverless functions that perform specific tasks in your data curation process. For example, Lambda functions can be triggered by events or scheduled to perform specific transformations.


    • Write Lambda functions to perform custom data transformations or enrichment tasks.
    • Configure Lambda triggers based on events or schedules.
  1. Amazon S3 Event Notifications:
  • Use Amazon S3 event notifications to trigger Lambda functions or other processes when new raw data is uploaded to the raw S3 bucket.


    • Set up S3 event notifications to trigger Lambda functions or other processes when new objects are created or modified in the raw S3 bucket.
  1. AWS Glue DataBrew:
  • AWS Glue DataBrew is a visual data preparation tool that helps you clean and normalize data without writing code. It can be integrated into your data curation workflow.


    • Use Glue DataBrew to visually explore and clean data.
    • Create recipes to transform and curate data interactively.
  1. Amazon S3 Cross-Region Replication:
  • If needed, you can use S3 Cross-Region Replication to replicate curated data from one S3 bucket to another in a different AWS region.


    • Set up S3 Cross-Region Replication for the curated data bucket.
  1. Logging and Monitoring:
  • Implement logging and monitoring to track the progress and performance of your data curation workflow. Utilize AWS CloudWatch Logs and CloudWatch Metrics.


  • Data Quality and Validation:
    • Implement data quality checks and validation steps to ensure the accuracy and integrity of curated data.
  • Access Control:
    • Configure proper access control on your S3 buckets to ensure that only authorized users and services can read or modify the data.
  • Cost Optimization:
    • Monitor and optimize costs associated with the storage and processing of data in S3 and other AWS services.
  • Incremental Data Processing:
    • Consider designing your workflow to support incremental data processing to handle new or updated data efficiently.

Example Workflow:

  • Raw Data Ingestion:
    • Raw data is uploaded to the raw S3 bucket.
  • Glue Crawler:
    • A Glue Crawler is scheduled to discover the schema of the raw data.
  • Glue ETL Job:
    • A Glue ETL job is executed to transform the raw data into a curated format, and the curated data is stored in the new S3 bucket.
  • Athena Table:
    • A table is created in Athena that references the curated data in the new S3 bucket.
  • Step Functions Orchestration:
    • Step Functions orchestrate the entire workflow, including invoking Lambda functions for specific tasks.
  • Monitoring and Logging:
    • CloudWatch Logs and Metrics are used for monitoring and logging throughout the workflow.

Remember to adapt these steps based on your specific requirements, and leverage the capabilities of AWS services to build a robust, scalable, and automated data curation process.



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