Data Engineering with GCP: Practical guide by Mahesh T V (.ePUB)
File Size: 14.0 MB
Data Engineering with GCP: Practical guide to designing and deploying scalable data pipelines on Google Cloud by Mahesh T V
Requirements: .ePUB reader, 14.0 MB | True EPUB
Overview: 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. The book is structured to start with the key foundation concepts of data engineering. It covers a wide range of GCP data engineering services for data ingestion, data storage, data warehouse and data transformation. The book also explains how the curated data can be used for analytics and Machine Learning. Finally, the book also covers advanced topics such as data migration, data sharing and emerging trends in the discipline of data engineering in GCP. This book is for data architects, data engineers, data analysts, and ML engineers working on transforming raw data to curated, quality data for enterprise consumption. It caters to beginners as well as experienced data professionals and students who want to become data professionals.
Genre: Non-Fiction > Tech & Devices
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