You're reading from Data Engineering with Google Cloud Platform A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781835080115

Length 476 pages

Edition 2nd Edition

Languages

SQL

Tools

Google Cloud Platform

Concepts

Data Engineering

Author (1):

Adi Wijaya

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1: Getting Started with Data Engineering with GCP FREE CHAPTER

2. Chapter 1: Fundamentals of Data Engineering

3. Chapter 2: Big Data Capabilities on GCP

4. Part 2: Build Solutions with GCP Components

5. Chapter 3: Building a Data Warehouse in BigQuery

6. Chapter 4: Building Workflows for Batch Data Loading Using Cloud Composer

7. Chapter 5: Building a Data Lake Using Dataproc

8. Chapter 6: Processing Streaming Data with Pub/Sub and Dataflow

9. Chapter 7: Visualizing Data to Make Data-Driven Decisions with Looker Studio

10. Chapter 8: Building Machine Learning Solutions on GCP

11. Part 3: Key Strategies for Architecting Top-Notch Solutions

12. Chapter 9: User and Project Management in GCP

13. Chapter 10: Data Governance in GCP

14. Chapter 11: Cost Strategy in GCP

15. Chapter 12: CI/CD on GCP for Data Engineers

16. Chapter 13: Boosting Your Confidence as a Data Engineer

17. Index

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In data engineering, the definition of a workflow is a set of configurations to automate tasks, jobs, and their dependencies. If we are talking about database workflow, we talk about how to automate the table creation process.

The main objects in any database system are tables, and one of the main differences between an application database and a data warehouse is the creation of tables. Compared to tables in application databases, where tables are mostly static and created to support frontend applications, tables in data warehouses are dynamic. The table is the main product as it is a collection of business logic, and data flows.

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