Can you describe the project and the architecture you followed for delivering an AWS solution, including data ingestion and transformations?

In my recent project, we built a data lake on AWS to ingest streaming and batch data for analytics. The architecture starts with data ingestion via Amazon Kinesis Data Streams for real‑time logs and S3 event notifications for batch uploads. Raw files are stored in an S3 "raw" bucket. An AWS Glue crawler catalogs the data into the Glue Data Catalog. For transformations, we use Glue ETL jobs written in PySpark to clean, deduplicate, and enrich the data, writing the results to a "processed" S3 bucket and loading them into Amazon Redshift for reporting. We also expose the data via Amazon Athena for ad‑hoc queries. The solution is fully serverless, cost‑effective, and scales automatically. I was responsible for designing the data flow, defining the Glue jobs, and setting up IAM roles and security groups to ensure data privacy.