> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Course Overview
> Overview of a hands-on course preparing learners for the Google Cloud Professional Data Engineer certification, covering GCP data services, ETL, orchestration, security, ML integration, and exam labs.
Hello data, AI, and ML learners — welcome. This course will take you from core cloud basics to advanced data engineering on Google Cloud Platform (GCP). In today's AI-driven world, high-quality data is the fuel powering intelligent systems. This Certified Data Engineer course helps you build reliable, scalable data solutions so your models and analytics can perform in production.
I’m Raghunandana Krishnamurthy, and I’ll guide you through focused lessons and hands-on labs on GCP. No prior cloud experience is required.
This course is designed for data engineers, ML engineers, analysts, and cloud practitioners who want practical, production-ready skills on GCP and preparation for the Professional Data Engineer certification.
What you'll learn (high level)
* Fundamentals and setup
* Core cloud concepts and setting up a GCP account and project.
* Overview of Google Cloud services used in data engineering.
* Core data services
* Cloud Storage for scalable object storage.
* BigQuery for serverless, petabyte-scale analytics.
* Cloud SQL for managed relational databases.
* Data processing and ETL
* Build batch and streaming pipelines with Dataflow.
* Run large-scale Hadoop/Spark workloads on Dataproc.
* Prepare and transform data using Dataprep.
* Orchestration, monitoring, and cost control
* Automate workflows with Cloud Composer (Apache Airflow).
* Monitor pipelines and optimize costs for production workloads.
* Security, governance, and metadata
* Secure data, meet compliance needs, and manage metadata using Data Catalog.
* Machine learning integration
* Build and deploy models with BigQuery ML and integrate ML into ETL pipelines.
* Exam preparation
* Full-length practice exams, exam-focused labs, and performance-based scenario practice.
Module summary
| Module | Key services | What you’ll be able to do |
| --------------------------- | ---------------------------------- | ----------------------------------------------------------------- |
| Fundamentals & Setup | Cloud Console, IAM, Billing | Configure GCP projects, users, and permissions |
| Storage & Warehousing | Cloud Storage, BigQuery, Cloud SQL | Design storage/warehouse solutions for analytics |
| Data Processing | Dataflow, Dataproc, Dataprep | Implement ETL/ELT for batch and streaming data |
| Orchestration & Monitoring | Cloud Composer, Cloud Monitoring | Schedule workflows, monitor pipelines, alerting |
| Security & Governance | IAM, VPC, Data Catalog | Apply security best practices and metadata management |
| ML Integration & Deployment | BigQuery ML, AI Platform | Train, evaluate, and deploy models integrated with data pipelines |
| Exam Prep & Labs | Practice exams, hands-on labs | Validate skills and readiness for certification |
Hands-on labs and learning path
* Guided labs that mirror real-world scenarios and common interview/certification tasks.
* Step-by-step walkthroughs for designing data pipelines, tuning BigQuery schemas and queries, and building streaming architectures.
* Exam-style practice questions and full-length mock tests aligned to the Professional Data Engineer objectives.
Community and learning support
At KodeKloud we believe collaborative learning accelerates progress. Join our active community forums to:
* Ask questions and troubleshoot labs.
* Share notes and implementation patterns.
* Collaborate on study groups and exam strategy.
Practical experience is essential. Use the labs to practice in a real GCP project — be mindful of GCP billing. Clean up resources after labs to avoid unexpected charges.
Why this course is valuable
* Learn GCP-native patterns for scalable, maintainable data systems.
* Focus on production-ready practices: security, cost control, monitoring, and operational excellence.
* Prepare specifically for the Professional Data Engineer certification with targeted labs and mock exams.
Links and references
* [Google Cloud: Professional Data Engineer certification guide](https://cloud.google.com/certification/data-engineer)
* [BigQuery documentation](https://cloud.google.com/bigquery/docs)
* [Dataflow documentation](https://cloud.google.com/dataflow/docs)
* [Dataproc documentation](https://cloud.google.com/dataproc/docs)
* [Cloud Composer (Airflow) docs](https://cloud.google.com/composer/docs)
Ready to begin? Enroll now, join the community, and start building production-quality data solutions on Google Cloud.