book

Data Engineering with Google Cloud Platform

Name: Data Engineering with Google Cloud Platform
Author: Adi Wijaya
ISBN: 9781800561328

by Adi Wijaya

March 2022

Beginner to intermediate

440 pages

9h 43m

English

Packt Publishing

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Data Engineering with Google Cloud Platform
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1: Getting Started with Data Engineering with GCP
Chapter 1: Fundamentals of Data Engineering
Understanding the data life cycleUnderstanding the need for a data warehouseKnowing the roles of a data engineer before startingData engineer versus data scientistThe focus of data engineersFoundational concepts for data engineeringETL concept in data engineering The difference between ETL and ELTWhat is NOT big data?A quick look at how big data technologies store dataA quick look at how to process multiple files using MapReduceSummaryExerciseSee also
Chapter 2: Big Data Capabilities on GCP
Technical requirementsUnderstanding what the cloud isThe difference between the cloud and non-cloud eraThe on-demand nature of the cloud Getting started with Google Cloud PlatformIntroduction to the GCP console Practicing pinning servicesCreating your first GCP projectUsing GCP Cloud ShellA quick overview of GCP services for data engineeringUnderstanding the GCP serverless serviceService mapping and prioritizationThe concept of quotas on GCP servicesUser account versus service accountSummary
Section 2: Building Solutions with GCP Components
Chapter 3: Building a Data Warehouse in BigQuery
Technical requirementsIntroduction to Google Cloud Storage and BigQueryBigQuery data locationIntroduction to the BigQuery consoleCreating a dataset in BigQuery using the consoleLoading a local CSV file into the BigQuery tableUsing public data in BigQueryData types in BigQuery compared to other databasesTimestamp data in BigQuery compared to other databasesPreparing the prerequisites before developing our data warehouseStep 1: Access your Cloud shellStep 2: Check the current setup using the command line Step 3: The gcloud init commandStep 4: Download example data from GitStep 5: Upload data to GCS from GitPracticing developing a data warehouseData warehouse in BigQuery – Requirements for scenario 1Steps and planning for handling scenario 1Data warehouse in BigQuery – Requirements for scenario 2Steps and planning for handling scenario 2SummaryExercise – Scenario 3See also
Chapter 4: Building Orchestration for Batch Data Loading Using Cloud Composer
Technical requirementsIntroduction to Cloud ComposerUnderstanding the working of Airflow Provisioning Cloud Composer in a GCP projectExercise: Build data pipeline orchestration using Cloud ComposerLevel 1 DAG – Creating dummy workflowsLevel 2 DAG – Scheduling a pipeline from Cloud SQL to GCS and BigQuery datasetsLevel 3 DAG – Parameterized variablesLevel 4 DAG – Guaranteeing task idempotency in Cloud ComposerLevel 5 DAG – Handling late data using a sensorSummary
Chapter 5: Building a Data Lake Using Dataproc
Technical requirementsIntroduction to DataprocA brief history of the data lake and Hadoop ecosystemA deeper look into Hadoop componentsHow much Hadoop-related knowledge do you need on GCP? Introducing the Spark RDD and the DataFrame conceptIntroducing the data lake conceptHadoop and Dataproc positioning on GCPExercise – Building a data lake on a Dataproc clusterCreating a Dataproc cluster on GCPUsing Cloud Storage as an underlying Dataproc file system Exercise: Creating and running jobs on a Dataproc clusterPreparing log data in GCS and HDFSDeveloping Spark ETL from HDFS to HDFSDeveloping Spark ETL from GCS to GCSDeveloping Spark ETL from GCS to BigQueryUnderstanding the concept of the ephemeral clusterPracticing using a workflow template on DataprocBuilding an ephemeral cluster using Dataproc and Cloud ComposerSummary
Chapter 6: Processing Streaming Data with Pub/Sub and Dataflow
Technical requirementsProcessing streaming dataStreaming data for data engineersIntroduction to Pub/SubIntroduction to DataflowExercise – Publishing event streams to cloud Pub/SubCreating a Pub/Sub topicCreating and running a Pub/Sub publisher using PythonCreating a Pub/Sub subscriptionExercise – Using Cloud Dataflow to stream data from Pub/Sub to GCSCreating a HelloWorld application using Apache BeamCreating a Dataflow streaming job without aggregationCreating a streaming job with aggregationSummary

Chapter 7: Visualizing Data for Making Data-Driven Decisions with Data Studio
Technical requirementsUnlocking the power of your data with Data StudioFrom data to metrics in minutes with an illustrative use caseUnderstanding what BigQuery INFORMATION_SCHEMA isExercise – Exploring the BigQuery INFORMATION_SCHEMA table using Data StudioExercise – Creating a Data Studio report using data from a bike-sharing data warehouseUnderstanding how Data Studio can impact the cost of BigQueryWhat kind of table could be 1 TB in size? How can a table be accessed 10,000 times in a month? How to create materialized views and understanding how BI Engine worksUnderstanding BI EngineSummary
Chapter 8: Building Machine Learning Solutions on Google Cloud Platform
Technical requirementsA quick look at machine learningExercise – practicing ML code using PythonPreparing the ML dataset by using a table from the BigQuery public datasetTraining the ML model using Random Forest in PythonCreating Batch Prediction using the training dataset's outputThe MLOps landscape in GCPUnderstanding the basic principles of MLOpsIntroducing GCP services related to MLOpsExercise – leveraging pre-built GCP models as a service Uploading the image to a GCS bucketCreating a detect text function in PythonExercise – using GCP in AutoML to train an ML modelExercise – deploying a dummy workflow with Vertex AI PipelineCreating a dedicated regional GCS bucketDeveloping the pipeline on PythonMonitoring the pipeline on the Vertex AI Pipeline consoleExercise – deploying a scikit-learn model pipeline with Vertex AICreating the first pipeline, which will result in an ML model file in GCSRunning the first pipeline in Vertex AI PipelineCreating the second pipeline, which will use the model file from the prediction results as a CSV file in GCSRunning the second pipeline in Vertex AI PipelineSummary
Section 3: Key Strategies for Architecting Top-Notch Data Pipelines
Chapter 9: User and Project Management in GCP
Technical requirements Understanding IAM in GCP Planning a GCP project structureUnderstanding the GCP organization, folder, and project hierarchyDeciding how many projects we should have in a GCP organizationControlling user access to our data warehouseUse-case scenario – planninga BigQuery ACL on an e-commerce organizationColumn-level security in BigQueryPracticing the concept of IaC using TerraformExercise – creating and running basic Terraform scriptsSelf-exercise – managing a GCP project and resources using TerraformSummary
Chapter 10: Cost Strategy in GCP
Technical requirementsEstimating the cost of your end-to-end data solution in GCPComparing BigQuery on-demand and flat-rate Example – estimating data engineering use caseTips for optimizing BigQuery using partitioned and clustered tables Partitioned tablesClustered tablesExercise – optimizing BigQuery on-demand costSummary
Chapter 11: CI/CD on Google Cloud Platform for Data Engineers
Technical requirements Introduction to CI/CDUnderstanding the data engineer's relationship with CI/CD practicesUnderstanding CI/CD components with GCP servicesExercise – implementing continuous integration using Cloud BuildCreating a GitHub repository using Cloud Source RepositoryDeveloping the code and Cloud Build scriptsCreating the Cloud Build TriggerPushing the code to the GitHub repositoryExercise – deploying Cloud Composer jobs using Cloud Build Preparing the CI/CD environmentPreparing the cloudbuild.yaml configuration filePushing the DAG to our GitHub repositoryChecking the CI/CD result in the GCS bucket and Cloud ComposerSummaryFurther reading
Chapter 12: Boosting Your Confidence as a Data Engineer
Overviewing the Google Cloud certificationExam preparation tipsExtra GCP services materialQuiz – reviewing all the concepts you've learned aboutQuestionsAnswersThe past, present, and future of Data EngineeringBoosting your confidence and final thoughtsSummary
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Overview

In 'Data Engineering with Google Cloud Platform', you'll explore how to construct efficient, scalable data pipelines using GCP services. This hands-on guide covers everything from building data warehouses to deploying machine learning pipelines, helping you master GCP's ecosystem.

What this Book will help me do

Build comprehensive data ingestion and transformation pipelines using BigQuery, Cloud Storage, and Dataflow.
Design end-to-end orchestration flows with Airflow and Cloud Composer for automated data processing.
Leverage Pub/Sub for building real-time event-driven systems and streaming architectures.
Gain skills to design and manage secure data systems with IAM and governance strategies.
Prepare for and pass the Professional Data Engineer certification exam to elevate your career.

Author(s)

Adi Wijaya is a seasoned data engineer with significant experience in Google Cloud Platform products and services. His expertise in building data systems has equipped him with insights into the real-world challenges data engineers face. Adi aims to demystify technical topics and deliver practical knowledge through his writing, helping tech professionals excel.

Who is it for?

This book is tailored for data engineers and data analysts who want to leverage GCP for building efficient and scalable data systems. Readers should have a beginner-level understanding of topics like data science, Python, and Linux to fully benefit from the material. It is also suitable for individuals preparing for the Google Professional Data Engineer exam. The book is a practical companion for enhancing cloud and data engineering skills.

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Data Engineering with Google Cloud Platform - Second Edition

Publisher Resources

ISBN: 9781800561328

Cloud Computing

Data Engineering

Data Science

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