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 6: Processing Streaming Data with Pub/Sub and Dataflow

Processing streaming data is becoming increasingly popular, as streaming enables businesses to get real-time metrics on business operations. This chapter describes which paradigm should be used—and when—for streaming data. The chapter will also cover how to apply transformations to streaming data using Cloud Dataflow, and how to store processed records in BigQuery for analysis.

Learning about streaming data is easier when we really do it, so we will exercise creating a streaming data pipeline on Google Cloud Platform (GCP). We will use two GCP services, Pub/Sub and Dataflow. Both of the services are essential in creating a streaming data pipeline. We will use the same dataset as we used for practicing a batch data pipeline. With that, you can compare how similar and different the approaches are.

As a summary, here are the topics that we will discuss in this chapter:

  • Processing streaming data
  • ...