This project focuses on designing an enterprise-scale lakehouse architecture on GCP using Cloud Storage, BigLake, and BigQuery. It integrates structured and semi-structured customer data from multiple sources while enforcing governance through Dataplex and Data Catalog. The architecture supports BI reporting, ad-hoc analytics, and AI model training with optimized performance, security, and cost controls, reflecting real-world enterprise analytics platforms.

This project involves architecting a real-time streaming solution using Pub/Sub and Dataflow to ingest and process high-volume IoT sensor data. The system applies windowing, late-data handling, and exactly-once processing before storing curated outputs in BigQuery for real-time dashboards and alerts. The design mirrors production-grade streaming analytics systems used in manufacturing and smart infrastructure domains.

This project centers on designing a secure, compliant financial data warehouse on GCP using BigQuery and Cloud KMS. It implements fine-grained IAM, VPC Service Controls, data encryption, and audit logging to meet regulatory standards. The architecture supports high-performance analytical queries while ensuring data privacy, governance, and enterprise-grade security controls.