This project demonstrates the implementation of a comprehensive data pipeline using Google BigQuery as the primary data warehouse solution. The pipeline showcases modern data engineering practices including external data integration, table optimization strategies, and performance tuning techniques.
Repository: Data Pipeline with BigQuery
The project focuses on building a scalable, cost-effective data warehouse solution that can handle large volumes of NYC taxi trip data while maintaining optimal query performance and cost efficiency.
• OLAP vs OLTP: Understanding the fundamental differences between Online Analytical Processing and Online Transaction Processing systems • Data Warehousing: Implementing centralized storage for analytical workloads with optimized query performance • Table Partitioning: Dividing large tables into manageable chunks based on time or range values • Clustering: Organizing data within partitions to improve query performance and reduce costs • External Tables: Querying data stored outside BigQuery without incurring storage costs • Performance Optimization: Implementing best practices for cost reduction and query efficiency
This repository demonstrates workflow orchestration for data engineering pipelines using Kestra. It guides users through building, running, and scheduling data pipelines that extract, transform, and load (ETL) data both locally (with PostgreSQL) and in the cloud (with Google Cloud Platform). The project is hands-on and includes conceptual explanations, infrastructure setup, and several example pipeline flows.
This repository serves as a practical guide to building and orchestrating robust data pipelines using Apache Airflow. It covers essential concepts from basic workflow management to advanced deployments with Google Cloud Platform (GCP) and Kubernetes.
