Data Engineering on Google Cloud Platform Course Overview

Last Updated : 22 May 2025

Data Engineering on Google Cloud Platform Course Overview

The Data Engineering on Google Cloud Platform course is designed to equip learners with essential skills for building and managing robust, scalable data solutions on Google Cloud. Through a comprehensive curriculum, participants will delve into a variety of Google Cloud technologies, including Dataproc, BigQuery, Dataflow, and more, learning how to process big data, integrate with machine learning APIs, and handle streaming data.

Starting with Module 1, students will gain hands-on experience with Google Cloud Dataproc by creating and managing Hadoop clusters, scaling resources, and optimizing costs with preemptible worker nodes. In subsequent modules, learners will run Dataproc jobs, integrate with other GCP services, and explore serverless data analysis with BigQuery. They'll also develop data pipelines with Dataflow, harness machine learning with Tensorflow and CloudML, and understand streaming analytics.

By the end of the course, participants will have the practical knowledge to design and deploy data processing systems, enabling them to extract insights from large datasets and make data-driven decisions. This course is vital for those looking to advance their careers in the field of data engineering.

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Flexi Video	16,449
Official E-coursebook
Exam Voucher (optional)
Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

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1-on-1 Training

Schedule personalized sessions based upon your availability.

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Customized Training

Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

Learn More

Happiness Guaranteed

Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

Learn More

Destination Training

Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.

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Target Audience for Data Engineering on Google Cloud Platform

Learn how to leverage Google Cloud for data processing, machine learning, and analytics in Koenig Solutions' comprehensive Data Engineering course.

Data Engineers looking to specialize in Google Cloud services
Cloud Solution Architects seeking knowledge in data services and ML integration
IT Professionals aiming to transition into data-centric roles
Big Data Analysts interested in utilizing Google Cloud tools
Data Scientists requiring expertise in Google Cloud’s ML capabilities
Machine Learning Engineers exploring scalable cloud solutions
Software Developers and Engineers who work with data pipelines
DevOps Engineers focusing on continuous integration of data services
Business Intelligence Professionals seeking to leverage cloud analytics
Systems Administrators aiming to manage scalable data clusters
Data Analytics Managers needing to understand cloud-based data platforms
Technical Project Managers overseeing data engineering projects
Graduates in computer science or related fields aspiring to work with cloud data services
IT Consultants providing strategic advice on cloud data solutions

Learning Objectives - What you will Learn in this Data Engineering on Google Cloud Platform?

Introduction to Learning Outcomes

This course equips students with skills in data engineering on Google Cloud Platform, covering tools like Dataproc, BigQuery, Dataflow, TensorFlow, and Cloud ML for processing, analyzing, and leveraging big data and machine learning.

Learning Objectives and Outcomes

Understand the management and scaling of clusters using Google Cloud Dataproc, including creating, customizing, and deleting clusters.
Execute data processing jobs with Dataproc, utilizing Hadoop, Spark, Pig, and Hive, and grasp the concept of separation of storage and compute.
Integrate Dataproc with GCP services and employ initialization actions to customize clusters, with a focus on leveraging BigQuery.
Add machine learning capabilities to data analysis with Google's ML APIs and identify common use cases for machine learning.
Perform serverless data analysis using BigQuery, including writing complex queries, managing data ingestion/exporting, and optimizing for performance and cost.
Develop scalable, autoscaling data pipelines with Google Dataflow and Apache Beam, and process both batch and stream data.
Get introduced to machine learning fundamentals, create ML datasets, and start building ML models with TensorFlow.
Experience hands-on training with TensorFlow for building, training, and monitoring ML models, including the use of low-level TensorFlow operations.
Scale ML models with GCP's Cloud ML, understanding the end-to-end training process and the packaging of TensorFlow models for cloud-based training.
Apply feature engineering techniques to improve model performance and learn data preprocessing with Cloud ML.
Design streaming analytics pipelines to handle real-time data processing, understanding challenges such as handling variable data volumes and late-arriving data.
Implement streaming pipelines using Cloud Pub/Sub and Google Dataflow, focusing on robust handling of live data streams.
Create streaming analytics and dashboards using tools like BigQuery and Google Data Studio for real-time data visualization and decision-making.
Learn about high throughput and low-latency data solutions with Bigtable and Cloud Spanner, including schema design and data ingestion strategies.