GCP Data Engineer Course

SKU: 3081
11 Lesson
|
30 Hours
Advance your data engineering skills with igmGuru’s Data Engineering on GCP course. This comprehensive program provides in-depth knowledge of designing, building and managing scalable data pipelines using Google Cloud services such as Cloud Storage, BigQuery, Dataflow, Dataproc, Pub/Sub and Cloud Composer. Through hands-on learning and real-world scenarios, you will gain practical experience in batch and streaming data processing, data warehousing, orchestration, security, monitoring and cost optimization on GCP. Designed for professionals aiming to build reliable, production-ready data platforms, this course equips you with the essential skills required to succeed as a Google Cloud data engineer.
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Google Cloud Platform GCP Data Engineer Course

Overview

GCP Data Engineer Course introduces the architecture and services used to collect, transform, store, and analyze data on Google Cloud. You will learn how to design data solutions that support analytics, operational workloads, and business intelligence while following Google Cloud data engineering best practices.

Prerequisites for Google Cloud and Data Engineering Course

  • Basic knowledge of cloud computing
  • Familiarity with programming (Python or Java)
  • Understanding of data structures and algorithms
  • Experience with SQL and relational databases
  • Knowledge of data engineering concepts (ETL, data pipelines)
  • Basic understanding of networking concepts
  • Familiarity with Linux/Unix operating systems
  • Experience with version control (Git)
  • Understanding of machine learning basics (recommended but not mandatory)

What Will You Learn

  • Google Cloud Platform core services and architecture
  • Data engineer roles and responsibilities on GCP
  • GCP resource hierarchy, regions, and zones
  • Cloud Storage and data lake design on GCP
  • Data ingestion techniques and file formats
  • BigQuery architecture, datasets, and tables
  • Writing and optimizing SQL queries in BigQuery
  • BigQuery cost management and optimization
  • Batch data processing using Dataflow and Dataproc
  • Apache Beam fundamentals
  • Real-time data processing with Pub/Sub
  • Streaming data pipelines with Dataflow
  • Data orchestration using Cloud Composer (Airflow)
  • Visual ETL with Cloud Data Fusion
  • End-to-end data pipeline design on GCP
  • Operational databases using Cloud SQL and Spanner
  • Wide-column storage with Cloud Bigtable
  • Change Data Capture (CDC) patterns
  • BigQuery ML and SQL-based machine learning
  • Vertex AI basics for ML workflows
  • Data security and IAM on Google Cloud
  • Data governance with Data Catalog and DLP
  • Monitoring and logging data pipelines
  • Designing reliable and fault-tolerant data architectures
  • Cost optimization and best practices for GCP data services

Who Should Do This Training

  • Aspiring data engineers
  • Data analysts transitioning to data engineering
  • Software engineers working with data pipelines
  • Cloud engineers using Google Cloud Platform
  • Big data professionals
  • ETL and data warehouse developers
  • Analytics engineers
  • Database administrators
  • Machine learning engineers working with data pipelines
  • IT professionals involved in data and cloud projects

Key Features

Course Curriculum

1. Overview of Google Cloud Platform services
2. Data engineer roles and responsibilities
3. GCP resource hierarchy: organizations, folders, projects
4. Regions, zones and multi-region concepts
1. Cloud Storage concepts: buckets, classes, lifecycle management
2. Designing data lakes on GCP
3. File formats: CSV, Parquet, Avro, ORC
4. Data ingestion into Cloud Storage
1. BigQuery architecture and storage
2. Datasets, tables, views, partitions, clustering
3. Standard SQL in BigQuery
4. Designing schemas and optimizing queries
5. BigQuery pricing and cost optimization
1. Introduction to Dataflow and Apache Beam
2. Building batch pipelines with Dataflow
3. Using Dataproc for Spark and Hadoop workloads
4. When to choose Dataflow vs Dataproc
1. Pub/Sub fundamentals (topics, subscriptions, message flow)
2. Streaming pipelines with Dataflow and Pub/Sub
3. Windowing, triggers and late data handling
4. Streaming inserts into BigQuery
1. Cloud Data Fusion overview (visual ETL)
2. Cloud Composer (Apache Airflow) and workflow orchestration
3. Scheduling jobs with Cloud Scheduler and Workflows
4. Building end-to-end data pipelines
1. Cloud SQL (MySQL/Postgres), Cloud Spanner basics
2. Cloud Bigtable for wide-column storage
3. Integrating OLTP sources with BigQuery and Dataflow
4. Change data capture (CDC) patterns
1. BigQuery ML: creating and using ML models in SQL
2. Vertex AI overview for model training, deployment and prediction
3. Feature engineering using Dataflow and BigQuery
4. Integrating ML predictions into data pipelines
1. IAM roles and permissions for data services
2. VPC Service Controls and private access
3. Data Catalog: metadata management and discovery
4. Data Loss Prevention (DLP) and encryption options
1. Cloud Logging and Cloud Monitoring for data pipelines
2. Setting up alerts and SLOs for Dataflow/BigQuery jobs
3. Debugging and performance tuning pipelines
4. Designing reliable, fault-tolerant data architectures
1. Cost optimization for BigQuery, Dataflow, Storage
2. Quotas, limits and scaling considerations
3. Designing efficient schemas and pipelines
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Course Fees

Online Class Room Program

US $ 799.00
100% Money Back Guarantee
  • Duration : 30 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 19 Aug 2026
  • Weekday Batch 24 Aug 2026
  • Weekend Batch 22 Aug 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support
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Google Cloud Data Engineering Certification Training

This course aligns with the Google Cloud Professional Data Engineer certification.

Certification Name: Google Cloud Professional Data Engineer

Exam Format

  • Exam Name: Professional Data Engineer
  • Mode: Online proctored or onsite test center (Kryterion)
  • Question Type: 40-50 Multiple-choice and multiple-select
  • Duration: 2 hours
  • Passing Score: Not publicly disclosed by Google
  • Language: English, Japanese
  • Attempts: Unlimited (14-day waiting period applies after a failed attempt)

Course Credential

Learners also receive an igmGuru Course Completion Certificate, validating practical knowledge in:

  • Designing data processing systems on Google Cloud
  • Building batch and streaming data pipelines
  • Data warehousing with BigQuery
  • Dataflow and Dataproc implementations
  • Data security, governance, and monitoring on GCP
Google Cloud Data Engineering Certification Training

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