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

Exercise: Creating and running jobs on a Dataproc cluster

In this exercise, we will try two different methods to submit a Dataproc job. In the previous exercise, we used the Spark shell to run our Spark syntax, which is common when practicing but not common in real development. Usually, we would only use the Spark shell for initial checking or testing simple things. In this exercise, we will code Spark jobs in editors and submit them as jobs. 

Here are the scenarios that we want to try:

  • Preparing log data in GCS and HDFS
  • Developing Spark ETL from HDFS to HDFS
  • Developing Spark ETL from GCS to GCS
  • Developing Spark ETL from GCS to BigQuery

Let's look at each of these scenarios in detail.

Preparing log data in GCS and HDFS

The log data is in our GitHub repository, located here:

https://github.com/PacktPublishing/Data-Engineering-with-Google-Cloud-Platform/tree/main/chapter-5/dataset/logs_example

If you haven't cloned the repository...