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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

By : Adi Wijaya
4.7 (12)
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Data Engineering with Google Cloud Platform

Data Engineering with Google Cloud Platform

4.7 (12)
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)
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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

Exercise – Publishing event streams to cloud Pub/Sub

In this exercise, we will try to stream data from Pub/Sub publishers. The goal is to create a data pipeline that can stream the data to a BigQuery table, but instead of using a scheduler (as we did in Chapter 4, Building Orchestration for Batch Data Loading Using Cloud Composer), we will submit a Dataflow job that will run as an application to flow data from Pub/Sub to a BigQuery table. In the exercise, we will use the bike-sharing dataset we used in Chapter 3, Building a Data Warehouse in BigQuery. Here are the overall steps in this Pub/Sub section:

  1. Creating a Pub/Sub topic
  2. Creating and running a Pub/Sub publisher using Python
  3. Creating a Pub/Sub subscription

Let's start by creating a Pub/Sub topic in the next section.

Creating a Pub/Sub topic

We can create Pub/Sub topics using many approaches—for example, using the GCP console, the gcloud command, or through code. As a starter, let...

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