دانلود کتاب بیگ‌کوئری (BigQuery) برای مهندسی داده – استاد شدن در بیگ‌کوئری، مدل‌سازی داده، dbt، جریان داده، آپاچی بیم، تحلیل جریانی، بیگ‌کوئری ام‌ال و مهندسی داده عملیاتی

Modern data engineering is not just writing SQL anymore — it is building systems that ingest, transform, and serve data reliably at scale. This book covers BigQuery and the tools around it on Google Cloud. BigQuery handles the analytics, but using it effectively in production means understanding the full ecosystem — dbt for transformations, Apache Beam and Dataflow for batch and stream processing, Cloud Composer for orchestration, and Pub/Sub for real-time messaging. Most professionals know pieces of this stack. Few understand how they work together. That is the gap this book fills. Every chapter includes runnable code, real performance numbers, and production patterns — not simplified demos. You will work with ecommerce datasets, see actual query costs and bytes scanned, and build complete pipelines you can adapt to your own projects. The book assumes basic SQL knowledge and access to a Google Cloud account. Some Python helps for the Beam and Spark chapters but is not required for most of the book. This book is divided into 15 chapters. Chapters 1 and 2 cover the foundations — what data engineering looks like today, where BigQuery fits, and how it works internally. Understanding slots, columnar storage, and the execution engine will change how you approach everything that follows. Chapter 3 walks through getting data into BigQuery — console uploads, Cloud Storage loads, file formats, Data Transfer Service, and streaming ingestion with Pub/Sub. Chapters 4 and 5 focus on modeling and optimization. You will compare nested, star schema, and denormalized approaches with actual performance data, and then see how partitioning and clustering can reduce scan costs dramatically. Chapter 6 digs into cost engineering — using INFORMATION_SCHEMA to find expensive queries, choosing between pricing models, and leveraging materialized views and BI Engine. Chapter 7 introduces dbt. This is where the book shifts from individual queries to building modular, tested, documented transformation pipelines. The lab builds a complete ELT system with staging, intermediate, and smart layers. Chapters 8 and 9 cover Apache Beam with Dataflow and Spark with BigQuery. You can build batch and streaming pipelines, and learn when each tool is the right choice. Chapter 10 brings orchestration with Cloud Composer — coordinating BigQuery, Dataflow, and dbt into automated workflows with retry logic and failure handling. Chapter 11 tackles security, governance, and data quality — from IAM and row-level security to data quality assertions in SQL. Chapter 12 shows how to train ML models using SQL in BigQuery, and deploy them to Vertex AI for real-time serving. Chapter 13 builds a production streaming pipeline end to end — Pub/Sub, Beam windowing, late data handling, and live BigQuery dashboards. Chapter 14 is the capstone project. You will be able to build a complete enterprise warehouse with incremental models, automated testing, and CI/CD deployment using GitHub Actions. Chapter 15 looks at where data engineering is heading — streaming-first architectures, Data Mesh, and AI-native platforms. Thus, by the end of the book, you will have all the skills to build production data platforms on Google Cloud — not just understand them.