Design

I embarked on a quest to design a data pipeline architecture that could leverage a combination of powerful AWS services:

Amazon S3: The backbone of our pipeline, providing scalable and durable storage for both raw and processed data. AWS Lambda: Serverless functions for data validation, transformation, and enrichment, triggered automatically by events in S3. AWS Glue (Optional): For more complex transformations or scenarios where a fully managed ETL service was desired, I explored integrating AWS Glue. Our architecture followed a classic extract, transform, load (ETL) pattern:

Data Ingestion: Raw data would be securely uploaded to an S3 bucket. Data Processing: Lambda functions, or potentially Glue jobs, would spring into action, validating, cleaning, and transforming the data. Data Storage: The processed data would be stored in a separate S3 bucket, ready for analysis or loaded into a data warehouse or database. Monitoring and Alerts: CloudWatch alarms would be implemented to ensure the pipeline’s health and notify us of any anomalies. This modular design offered flexibility and scalability, allowing us to easily add new data sources, modify transformation steps, and adapt to evolving business needs.