Modernizing Data Pipeline with AWS Lake Formation

A key component of any data pipeline is orchestrating the flow of data between different processing stages. The architecture starts with data ingestion, where Amazon Data Firehose is utilized to ingest near real-time data, while Amazon RDS is employed for transactional data storage.

The next step involves data storage and processing, where Amazon S3 serves as the central repository for both near real-time ingested data and transactional data. To perform data transformations for data analysts and scientists, the solution utilizes AWS Glue DataBrew, a powerful no-code data preparation service. For more complex ETL processing, AWS Glue Studio enables data engineers and developers to create ETL jobs through a drag-and-drop interface or coding.

The prepared and cleansed data is then stored in a target S3 location, which is natively integrated with AWS Lake Formation. This service has been leveraged to efficiently create the data lake and provide fine-grained access control (row/column level) to different IAM users, ensuring robust data protection.

For data analytics, the solution employs Amazon Redshift, a fully managed data warehousing service, to query the data stored in S3. This allows the customer to access a managed data warehousing solution tailored to their needs.

Finally, the data visualization component of the solution utilizes Amazon QuickSight, a scalable, serverless, and embeddable BI service, to provide valuable insights to the customer.