Data Engineering with GCP Practical guide to designing and deploying scalable data pipelines on Google Cloud

Google Cloud Platform (GCP) has emerged as a premier leader in cloud analytics, making data engineering skills more critical than ever for modern business success. The current evolution of generative artificial intelligence (AI) and agentic AI has created a significant demand in the data engineering discipline since the accuracy and effectiveness of AI output primarily depend on the quality of data. Ensuring high-quality, curated data requires a robust and scalable data engineering platform that can cater to the velocity, veracity, and volume of data. Google is a pioneer in data engineering solutions, which are provided through its GCP. Many of the Fortune 500 companies leverage GCP's services for transforming petabytes of data for analytics, AI, and machine learning (ML). This book begins with data engineering essentials like ETL, ELT, and big data roles before moving into GCP environment setup and security. You will learn BigQuery for data warehousing and SQL optimization, followed by real-time ingestion using Pub/Sub, Dataflow, and Datastream. You will learn to integrate machine learning via Vertex AI pipelines. Finally, it will provide the skills to use the processed data for analytics, AI, and ML use cases. After finishing this book, you will possess the technical competence to design, build, and monitor professional-grade data solutions on Google Cloud. You will be ready to tackle real-world challenges, from automating complex workflows to leveraging AI for predictive analytics in any enterprise environment.

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