This project focuses on designing and implementing a scalable enterprise data warehouse using Google BigQuery on Google Cloud Platform. It manages data ingestion from multiple structured and semi-structured sources stored in Cloud Storage. ELT pipelines are built using Dataflow to cleanse, transform, and load data efficiently. Partitioning and clustering strategies are applied to optimize query performance and reduce costs. Access controls and data security policies are configured using IAM. The project reflects real-world analytics architectures used by large enterprises for business intelligence and reporting.

This project focuses on building a real-time data streaming and analytics platform using Pub/Sub and Dataflow on Google Cloud. It handles continuous ingestion of event data from multiple producers and processes streaming data using windowing, triggers, and transformations. Processed data is stored in BigQuery for near real-time analysis and dashboarding. Monitoring and alerting are implemented to ensure pipeline reliability and low latency. The project reflects real-world streaming analytics solutions used in high-volume, event-driven enterprise environments.

This project focuses on developing an automated end-to-end data pipeline orchestration system using Cloud Composer (Apache Airflow) on Google Cloud. It manages batch and streaming workflows, schedules dependent jobs, and handles retries and failure scenarios. Data pipelines integrate services such as Cloud Storage, Dataflow, Dataproc, and BigQuery. Monitoring, logging, and cost optimization techniques are applied to ensure operational efficiency. The project reflects real-world enterprise data engineering operations and production-grade pipeline management practices.