Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GCP

Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer

Key Features

- Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution
- Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines
- Discover tips to prepare for and pass the Professional Data Engineer exam

Book Description

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards.

Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP.

By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.

What you will learn

- Load data into BigQuery and materialize its output for downstream consumption
- Build data pipeline orchestration using Cloud Composer
- Develop Airflow jobs to orchestrate and automate a data warehouse
- Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster
- Leverage Pub/Sub for messaging and ingestion for event-driven systems
- Use Dataflow to perform ETL on streaming data
- Unlock the power of your data with Data Studio
- Calculate the GCP cost estimation for your end-to-end data solutions

Who this book is for

This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

在GCP上建立並部署自己的數據管道，做出關鍵的架構決策，並獲得自信，提升您作為數據工程師的職業生涯。

主要特點：
- 了解數據工程概念，數據工程師的角色，以及使用GCP建立解決方案的好處。
- 學習如何使用各種GCP產品來輸入、消耗和轉換數據，並編排管道。
- 發現準備並通過專業數據工程師考試的技巧。

書籍描述：
通過本書，您將了解高度可擴展的Google Cloud Platform（GCP）如何使數據工程師能夠從存儲和處理數據、工作流編排到通過可視化儀表板呈現數據的端到端數據管道。

從快速概述數據工程的基本概念開始，您將學習數據工程師的各種責任以及GCP在履行這些責任中的重要作用。隨著章節的進展，您將能夠利用GCP產品來使用Cloud Storage和BigQuery構建示例數據倉庫，並使用Dataproc構建數據湖。本書逐步介紹數據輸入、數據清理、轉換和與其他源頭集成的操作。您將學習如何設計用於數據治理的IAM，使用Vertex AI部署ML管道，利用預建的GCP模型作為服務，並使用Google Data Studio可視化數據以構建引人入勝的報告。最後，您將找到如何提升自己作為數據工程師的職業生涯、參加專業數據工程師認證考試並準備成為GCP數據工程專家的技巧。

通過閱讀本數據工程書籍，您將掌握執行核心數據工程任務並使用GCP構建高效ETL數據管道的技能。

您將學到：
- 將數據加載到BigQuery並將其輸出材料化以供下游使用。
- 使用Cloud Composer構建數據管道編排。
- 開發Airflow作業以編排和自動化數據倉庫。
- 構建Hadoop數據湖，創建臨時集群並在Dataproc集群上運行作業。
- 利用Pub/Sub進行消息傳遞和事件驅動系統的輸入。
- 使用Dataflow對流數據進行ETL。
- 利用Data Studio發揮數據的潛力。
- 計算您端到端數據解決方案的GCP成本估算。

本書適合數據工程師、數據分析師以及任何希望使用GCP設計和管理數據處理管道的人。如果您正在準備參加Google的專業數據工程師考試，您會發現本書很有用。需要具備初級水平的數據科學、Python編程語言和Linux命令的理解。對數據處理和雲計算有基本的理解將有助於您充分利用本書的內容。

1. Fundamentals of Data Engineering
2. Big Data Capabilities on GCP
3. Building a Data Warehouse in BigQuery
4. Building Orchestration for Batch Data Loading Using Cloud Composer
5. Building a Data Lake Using Dataproc
6. Processing Streaming Data with Pub/Sub and Dataflow
7. Visualizing Data for Making Data-Driven Decisions with Data Studio
8. Building Machine Learning Solutions on Google Cloud Platform
9. User and Project Management in GCP
10. Cost Strategy in GCP
11. CI/CD on Google Cloud Platform for Data Engineers
12. Boosting Your Confidence as a Data Engineer

1. 資料工程基礎
2. 在GCP上的大數據能力
3. 在BigQuery上建立資料倉庫
4. 使用Cloud Composer建立批次資料加載的編排
5. 使用Dataproc建立資料湖
6. 使用Pub/Sub和Dataflow處理流式資料
7. 使用Data Studio視覺化資料以做出數據驅動的決策
8. 在Google Cloud Platform上建立機器學習解決方案
9. GCP中的使用者和專案管理
10. GCP中的成本策略
11. Google Cloud Platform上的CI/CD（持續整合/持續部署）適用於資料工程師
12. 提升作為資料工程師的自信心