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

Summary

In this chapter, we've learned about streaming data and we learned how to handle incoming data as soon as data is created. Data is created using the Pub/Sub publisher client. In practice, you can use this approach by requesting the application developer to send messages to Pub/Sub as the data source, or a second option is to use a change data capture (CDC) tool. In GCP, you can use a Google provided tool for CDC called Datastream. CDC tools can be attached to the backend database like CloudSQL to publish data changes such as insert, update, and delete operations. We as data engineers are responsible for using Pub/Sub, as we've learned in this chapter.

The second part of streaming data is how to process the data. In this chapter, we've learned how to use Dataflow to handle continuously incoming data from Pub/Sub to aggregate it on the fly and store it in BigQuery tables. Do keep in mind that you can also handle data from Pub/Sub using Dataflow in batch manner...