Google Cloud Professional Data Engineer Certification

Data

Cloud

GCP

Certification

Google Cloud Professional Data Engineer Certification

Prepare for certification by learning to design, build, and secure data pipelines on GCP using services like BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.

Raghunandana Sanur

Staff Data Engineer & MLOps Engineer at Talabat

Google Cloud Professional Data Engineer Certification

Fill this form to get a notification when course is released.

Enroll for Free

Subscribe Now

Enroll Now

Already Subscribed? Log in

Enroll in this Course

Lessons

Challenges

148

Topics

What you’ll learn

Our students work at..

Description

Ready to become a certified Google Cloud Professional Data Engineer? In today’s data-driven world, organizations depend on experts who can harness the power of Google Cloud Platform (GCP) to design, build, and manage robust data solutions. This GCP Data Engineer Certification Course is your hands-on guide to earning one of the most sought-after credentials in cloud and data engineering.

What You’ll Learn:

Foundations of GCP:
- Get started with Google Cloud Platform by exploring its core services—including Compute Engine, Cloud Storage, and BigQuery.
- Understand GCP’s architecture, data security fundamentals, and billing concepts.
Designing Data Processing Systems:
- Learn to design scalable and reliable data pipelines using tools like Dataflow and Dataproc.
- Build real-time and batch processing systems that turn raw data into actionable insights for modern businesses.
Building and Managing Data Storage:
- Master different GCP storage options, from Cloud SQL and Spanner to Bigtable and Datastore.
- Learn how to choose the right storage solution for different data workloads and compliance requirements.
Data Ingestion and Transformation:
- Ingest data using Pub/Sub and Dataflow, handling streaming and batch data seamlessly.
- Clean, enrich, and transform data at scale using Google Cloud’s powerful ETL (Extract, Transform, Load) services.
Advanced Analytics with BigQuery:
- Unleash the full potential of BigQuery for lightning-fast SQL analytics.
- Optimize queries, manage cost controls, automate loads, and export results to downstream apps and visualizations.
Machine Learning Integration:
- Use AI Platform and BigQuery ML to create, train, evaluate, and deploy machine learning models directly in the cloud.
- Explore data science workflows, and see how cloud-based ML is transforming data engineering.
Monitoring, Security, and Best Practices:
- Monitor, troubleshoot, and optimize data pipelines for efficiency and reliability.
- Implement security, privacy, and compliance best practices, using IAM, Encryption, and audit logging features.
- Prepare for real-world scenarios and the GCP Professional Data Engineer certification exam with practical quizzes, and case studies.

Who Should Take This Course?

Aspiring data engineers, cloud architects, and IT professionals looking to upskill in Google Cloud.
Data scientists, analysts, or developers seeking to build end-to-end data solutions and automation in GCP.
Anyone preparing to ace the Google Cloud Professional Data Engineer certification exam.

Join us and gain the expertise, hands-on experience, and confidence to build scalable, secure data solutions on GCP—and propel your tech career to new heights!

About the instructor

Raghunandana Krishnamurthy is a seasoned Staff Data Engineer and MLOps expert, skilled in navigating both GCP and AWS cloud platforms to accelerate model development and deployment. His experience spans modernizing legacy data systems, architecting hybrid infrastructures, and ensuring data quality for diverse applications. He used to hold Associate AWS Solution Architect certification, Cloudera Hadoop Admin certification, Airflow certification, and Databricks Lakehouse certification

A technical leader and passionate trainer, Raghunandana excels at building and maintaining big data platforms, championing DevOps best practices, and fostering team alignment. With hands-on expertise in tools like SageMaker, VertexAI, Prometheus, Grafana, and extensive DevOps tools focusing on Data Engineering and MLOps.

No items found.

Course Content

Expand All

Introduction

Topics

Lesson Content

Module Content

Course Overview 03:00

How to Reach Out to KodeKloud and Engage with the Community

GCP Core Fundamentals & Understanding

Topics

Lesson Content

Module Content

Google's View of the Professional Data Engineer 09:07

Lifecycle of data in a company 03:50

GCP's Global Infrastructure 05:45

Legacy Use Cases 05:44

GCP Resource Hierarchy 03:29

GCP Resource Hierarchy – Project Identification Framework 02:41

Demo: GCP Console Overview 05:02

Quiz: GCP Core Fundamentals & Understanding

GCP Networking

Topics

Lesson Content

Module Content

Cloud VPC - Introduction 03:24

Cloud VPC - IP Addresses and Subnets 05:07

Demo: Creating a VPC and Subnet in GCP 05:46

Cloud VPC - Private Service Connect 03:36

Cloud VPC - Connecting to other networks 05:06

Cloud VPC - Project Isolation Techniques 04:00

Cloud NAT (Network Address Translation) 03:28

Cloud Firewall Rules 02:51

Identity and Access Management (IAM) in GCP

Topics

Lesson Content

Module Content

Cloud IAM - Introduction 04:01

Cloud IAM - Principles of Least Privilege 04:46

Cloud IAM - Permissions and Roles 05:39

Cloud IAM - Role Type Details 02:08

Cloud IAM - Types of Service Accounts 05:14

Cloud IAM - Groups 04:13

Cloud IAM - Access Approval 03:30

Org Level Roles 04:50

Demo: Cloud IAM - Roles and Permissions 05:00

Demo: Using Service account in local editor 06:07

Cloud Observability

Topics

Lesson Content

Module Content

Cloud Logging 04:53

Demo: Exploring Logs Explorer 04:11

Cloud Monitoring 04:24

Development & CI/CD

Topics

Lesson Content

Module Content

Google Cloud SDK Components & gcloud CLI 06:33

Demo: Hands on with gcloud CLI 06:28

Demo: Hands on creating complete infrastructure stack using gcloud CLI 15:01

Cloud Build serverless CI/CD platform 05:52

Cloud Build - YAMLs 02:52

Development and CI/CD Summary 02:26

Data Security & Encryption

Topics

Lesson Content

Module Content

Data Loss Prevention (DLP) API 03:03

DLP API - Integration with GCP Services 02:54

Cloud Key Management Service (KMS) 05:02

Data Ingestion Options

Topics

Lesson Content

Module Content

Data Ingestion Introduction 03:12

Batch vs. Streaming Ingestion Patterns 03:37

What is a Coupled System? 05:34

Cloud Pub/Sub: Intro, Topics, Subscriptions 04:50

Connecting to Cloud Pub/Sub 03:14

Cloud Pub/Sub – Pull/Push methods 03:38

Cloud Pub/Sub - Acknowledgement 04:14

Cloud Pub/Sub - Delivery Guarantees 03:56

Cloud Pub/Sub - Dead Letter Queue (DLQ) 05:01

Demo: Working with Pub/Sub 07:38

Demo: Hands on with Pub/Sub Subscription and its integration 07:50

Datastream: For Change Data Capture (CDC) 04:12

Data Storage Options

Topics

Lesson Content

Module Content

Structured, Unstructured, and Semi-structured Data 05:36

Google Cloud Storage (GCS): Overview and Bucket Setup 06:11

Demo: Setting up GCS Bucket 07:22

GCS - Storage Class, Lifecycle Rules, Versioning, and Retention 05:49

GCS - Availability and Failover Strategies 03:21

GCS Quick Summary 03:58

Database (SQL, NoSQL and memory)

Topics

Lesson Content

Module Content

Databases in GCP - SQL, NoSQL and Memory 01:01

Basics on Databases - OLTP and OLAP 02:58

Structured Datastore Options in GCP 02:27

Cloud SQL 03:37

Cloud Spanner 02:59

Cloud Spanner Internals 03:05

Demo: Cloud Spanner Setup and Table Creation 11:59

How does Cloud Spanner achieve Scalability & High Availability ? 03:48

TrueTime with GCP Cloud Spanner 04:51

Cloud Spanner Interleaved Tables 03:08

Demo: Cloud Spanner Interleaved Tables, Cascading Delete 07:42

Cloud SQL vs Cloud Spanner 05:09

Semi Structured Data Options in GCP 04:02

Understanding Bigtable 02:50

BigTable Data Model and Column Families 04:39

BigTable Row Key Design and Principles 04:20

BigTable - Quick Summary 03:28

Firestore: Intro, Data Modeling, Datastore Mode 04:01

Cloud Memorystore: Intro and Use Cases 04:36

Quick Summary on Database (SQL, NoSQL and memory) 05:00

Quiz: Database (SQL, NoSQL and memory)

Data Orchestration Options

Topics

Lesson Content

Module Content

Cloud Composer: Orchestrating Data Workflows 07:23

Cloud Composer: Orchestration Patterns & Triggering Methods 05:35

Cloud Composer: Monitoring , Security and IAM 05:40

Cloud Composer: Production Best Practices 04:29

Cloud Composer: GCP Integration and Real-World Use Cases 02:53

Cloud Workflows: Orchestration 04:38

Cloud Functions: Serverless Fundamentals 04:16

Cloud Functions:Performance Characteristics and Common Data Processing Patterns 03:53

Cloud Functions: Real-World Integration Examples 04:45

Cloud Functions: Best Practices and Optimization 03:08

Cloud Functions vs Cloud Workflows 04:01

Quick Summary : Data Orchestration Options 04:02

Quiz: Data Orchestration Options

Data Processing

Topics

Lesson Content

Module Content

Dataflow: Introduction 04:12

Data Transformation Pipeline 04:43

Windowing in Dataflow 04:50

Watermarks & Triggers 04:09

Pipeline Design & Optimization 03:30

Performance, Scaling & Cost Optimization 05:48

Dataflow Exam Summary 04:57

Dataproc - Introduction 04:28

Dataproc Autoscaling Features 07:20

Running Spark Jobs on Dataproc 03:59

Preemptible VMs on Dataproc 04:12

Dataproc Cluster Optimization and Sizing 02:55

Storage Options and Migration to Dataproc 04:53

Dataproc Summary 03:15

Data Integration & Transformation Tools

Topics

Lesson Content

Module Content

Data Fusion - Architecture & Core Components 03:21

Data Fusion - Studio, Use Cases and Data Wrangler 05:09

Data Fusion - Best Practices & Optimization 05:10

Cloud Dataprep by Trifacta 04:48

Data Fusion vs Dataprep by Trifacta 03:22

Data Warehouse & Analytics Options

Topics

Lesson Content

Module Content

BigQuery: Introduction 03:49

BigQuery: Core Components 04:06

BigQuery: Anatomy of BigQuery 07:28

BigQuery Data Structures: Datasets, Tables, Views 06:41

Demo: Dataset Creation, Data Uploads & Tables 08:31

Demo: Materialized Views 06:11

BigQuery: Table Partitioning and Clustering 04:25

BigQuery: Storage Types and Cost Management 04:09

BigQuery: Backing up Data 02:37

BigQuery: Spark-BigQuery Connector 02:52

BigQuery: Federated Queries 02:52

Looker Studio for Visualization 04:00

Quiz: Data Warehouse & Analytics Options

Machine Learning Options

Topics

Lesson Content

Module Content

Why Machine Learning in GCP Data Professional? 03:38

BigQuery ML for Data Engineers 03:26

BigQuery ML Model Types 03:09

BigQuery ML Workflow 03:12

Achieving Optimal BigQuery ML 02:59

Multi-Cloud & Lakehouse Solutions

Topics

Lesson Content

Module Content

BigQuery Omni: Cross-Cloud Analytics Revolution 07:30

BigQuery Omni vs Data Movement Tools 02:46

BigLake: Intro, Unified Security 03:55

BigLake vs External Tables 04:26

Quick Summary on Multi-Cloud & Lakehouse Solutions 03:19

Data Management and Governance

Topics

Lesson Content

Module Content

Data Management and Governance 03:36

Dataplex: The Unified Governance Layer 04:12

Data Catalog: The Discovery and Classification Engine 04:31

Analytics Hub: The Data Sharing Platform 04:17

Quick Summary on Data Catalog Dataplex 03:46

GCP Data Engineering Architecture and Landscape

Topics

Lesson Content

Module Content

GCP Data Engineering Landscape 04:22

Architecture - Real Time Stream Processing IoT 04:45

Architecture - Gaming Analytics 04:22

Architecture - Publisher side analysis 03:34

Architecture - Time Series Analysis 02:11

Architecture - Complex Event Processing 02:15

GCP Data Engineer Mock Exam

Topics

Lesson Content

Module Content

Your Practice Exam, Streamlined in Three Stages

GCP Data Engineering Mock Exam - 1

GCP Data Engineering Mock Exam - 2

Frequently Asked Questions

Who are KodeKloud courses for?

KodeKloud courses are designed for learners at different skill levels, from beginners to experienced IT professionals looking to develop or strengthen their technical skills.

Do KodeKloud courses include hands-on practice?

Yes. Many KodeKloud courses include hands-on labs and practical exercises that let you apply what you learn in real-world environments.

Do I need any prior experience to take a KodeKloud course?

Prerequisites vary by course. Each course page provides information about the recommended knowledge or experience you may need before starting.

Can I learn at my own pace?

Yes. KodeKloud courses are designed for self paced learning, so you can learn according to your schedule and revisit lessons and practice activities as needed.

Can I access KodeKloud courses on mobile devices?

Yes. KodeKloud can be accessed through supported web browsers and mobile devices, allowing you to learn from different devices.

Do KodeKloud courses provide certificates?

Certificate availability depends on the course and your KodeKloud plan. Check the individual course page or your account for the applicable certificate details.

How long does it take to complete a KodeKloud course?

Course completion time varies depending on the course length, your prior experience, and how much time you spend on hands-on practice.

Can I practice while taking the course?

Yes. Courses that include hands-on labs allow you to practice concepts as you progress through the lessons.

How do I start learning a KodeKloud course?

Choose a course that matches your learning goals, review its requirements and curriculum, and select the available subscription option to start learning.

Fill this form to get a notification when course is released.