Book Image

Data Engineering with Google Cloud Platform

By : Adi Wijaya
3 (1)
Book Image

Data Engineering with Google Cloud Platform

3 (1)
By: Adi Wijaya

Overview of this book

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.
Table of Contents (17 chapters)
1
Section 1: Getting Started with Data Engineering with GCP
4
Section 2: Building Solutions with GCP Components
11
Section 3: Key Strategies for Architecting Top-Notch Data Pipelines

Chapter 4: Building Orchestration for Batch Data Loading Using Cloud Composer

The definition of orchestration is a set of configurations to automate tasks, jobs, and their dependencies. If we are talking about database orchestration, 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 applications, tables in data warehouses are dynamic. Tables are products, collections of business logic, and data flows.

In this chapter, we will learn how to orchestrate our data warehouse tables from Chapter 3, Building a Data Warehouse in BigQuery. We will learn how to automate the table creations using a Google Cloud Platform (GCP) service called Cloud Composer. This will include how to create a new Cloud Composer environment...