2. Cloud Analytics & Security Data Systems
Overview
Designed, built, and optimized high-throughput cloud analytics pipelines aggregating multi-terabyte security telemetry, message verdicts, and threat metadata daily. Engineered distributed ETL pipelines in Python using Apache Airflow to orchestrate data extraction from Amazon S3, transformation via AWS Glue, and COPY-based bulk loading into AWS Redshift clusters. Re-indexed and tuned AWS Redshift distribution and sort keys for petabyte-scale telemetry, accelerating complex analytical aggregation queries by more than 40%. Migrated operational data stores to AWS RDS (PostgreSQL) to serve high-throughput transactional queries, and built secure FastAPI microservices delivering real-time metrics directly to executive dashboards and customer reporting portals.
Challenge
As processed email volume grew across commercial and government cloud environments, the analytics layer needed to ingest, transform, and serve reporting queries on tens of millions of threat events per day without database contention or slow dashboard queries.
Approach
- Structured ETL workflows in Python using Apache Airflow to orchestrate parallel data extraction from S3, transformation via AWS Glue, and COPY-based bulk loading into AWS Redshift.
- Re-indexed and tuned AWS Redshift table distribution and sort keys for petabyte-scale security telemetry, reducing query runtimes on heavy analytical dashboards.
- Migrated operational databases to AWS RDS (PostgreSQL) to handle high-throughput transactional queries from downstream REST microservices.
- Developed secure, token-authenticated REST microservices using FastAPI to deliver aggregated metrics and threat trends directly to downstream reporting interfaces, backed by automated pipeline health monitoring.
Outcome
Delivered reliable reporting across multi-million event daily workloads, with automated monitoring and alerting that drastically reduced pipeline downtime and eliminated silent data ingestion failures.