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Google Cloud Data Engineer

Live Online (VILT) & Classroom Corporate Training Course

Master Google Cloud's data engineering tools and prepare for the Google Cloud Professional Data Engineer certification. This course covers data storage, processing, and machine learning integration on Google Cloud.

Expert-Led VILT & Classroom Hands-On CloudLabs Certification Voucher Available
CloudLabs
Projects
Assessments
24/7 Support
Lifetime Access

Overview

TheGoogle CloudData Engineercertification training prepares learners to design, build, maintain, and troubleshoot data processing systems on Google Cloud. This course covers data management, processing, and machine learning on Google Cloud’s powerful data infrastructure, equipping participants to make data-driven decisions by leveraging Google Cloud solutions.

Objectives

At the end of Google Data Engineer training course, participants will be able to

  • Design and build data processing systems on Google Cloud
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
  • Derive business insights from extremely large datasets using Google BigQuery
  • Train, evaluate, and predict using machine learning models using Tensorflow and Cloud ML
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
  • Enable instant insights from streaming data

Prerequisites

Basic knowledge of SQL and data modeling. Familiarity with general cloud computing concepts. Experience with data warehousing or data pipelines. Fundamental understanding of programming (Python or Java recommended). Interest in using data to solve business problems and drive decisions.

Course Outline

  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes
  • Scaling and deleting Clusters

  • Running Pig and Hive jobs.
  • Separation of storage and compute.

  • Customize cluster with initialization actions.
  • BigQuery Support.

  • Google’s Machine Learning APIs
  • Common ML Use Cases
  • Invoking ML APIs
  • Serverless Data Analysis with Google BigQuery and Cloud Dataflow

  • What is BigQuery
  • Queries and Functions
  • Loading data into BigQuery
  • Exporting data from BigQuery
  • Nested and repeated fields
  • Querying multiple tables
  • Performance and pricing

  • The Beam programming model
  • Data pipelines in Beam Python
  • Data pipelines in Beam Java
  • Scalable Big Data processing using Beam
  • Incorporating additional data
  • Handling stream data
  • GCP Reference architecture
  • Serverless Machine Learning with TensorFlow on Google Cloud Platform

  • What is machine learning (ML)
  • Effective ML: concepts, types
  • ML datasets: generalization

  • Getting started with TensorFlow
  • TensorFlow graphs and loops + lab
  • Monitoring ML training

  • Why Cloud ML?
  • Packaging up a TensorFlow model
  • End-to-end training

  • Creating good features
  • Transforming inputs
  • Synthetic features
  • Preprocessing with Cloud ML
  • Building Resilient Streaming Systems on Google Cloud Platform

  • Stream data processing: Challenges
  • Handling variable data volumes
  • Dealing with unordered/late data

  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions

  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.

  • Streaming analytics: from data to decisions
  • Querying streaming data with BigQuery
  • What is Google Data Studio?

  • What is Cloud Spanner?
  • Designing Bigtable schema
  • Ingesting into Bigtable

Available Training Modes

Pick the format that fits your team.

Same authorised curriculum, same trainers, same hands-on cloud labs — delivered the way that works for you.

Live Online (VILT)

Real-time instructor-led sessions over Zoom or Teams. Same classroom, different time zones.

Most popular

Classroom

Face-to-face training delivered at your office, our Bengaluru centre, or any partner venue worldwide.

Onsite

Self-Paced

Recorded sessions plus 24/7 access to cloud labs and assessments. Learn at the pace that works for each engineer.

On-demand

Blended

Live workshops with self-paced reinforcement and project-based labs. Best for hybrid teams across regions.

Hybrid teams
All modes include: hands-on cloud labs, recordings, assessments, certificate of completion. Talk to a solutions advisor →

Our Training Process

How a course becomes measurable skill.

One contract, five steps, zero handoffs. From discovery to deployment, the same Synergific team owns the outcome — not a chain of vendors.

5 Steps from your scoping call to certified, productive engineers.
01

Discover & set goals

We start with a scoping call to understand your team's current skill level, target outcomes, deadlines, and certification needs — then translate that into a measurable success plan with named owners on both sides.

02

Curate the right path

We map the optimal learning path — instructor-led, self-paced, or blended — with hands-on cloud labs, prerequisite refreshers, and certification vouchers built in. No filler modules, no padded curriculum.

03

Deliver hands-on training

Authorised trainers run live sessions backed by 24/7 cloud labs and real-world projects. Theory and practice on the same day — learners stop forgetting concepts before they get to apply them.

04

Assess & mentor

Continuous skill checks, mock exams, and 1:1 mentoring keep the program honest. If anyone falls behind, we course-correct in-flight — you'll never find out at the end that two engineers couldn't keep up.

05

Certify & apply on the job

Voucher-backed certification, post-training office hours, and 30-day reinforcement so skills land on real work — not just on the exam scorecard. Success measured after the course ends, not before.

Client Stories

What our clients say

Voices from L&D leaders, architects, and program managers who’ve trusted us with their upskilling.