GCP Data Engineer Online Training in Hyderabad
Learn data and AI engineering on Google Cloud — BigQuery, Vertex AI, Vertex AI Pipelines, Gemini and RAG, Apache Airflow and Cloud Composer, and MLOps on GCP — in live online classes.
G4.7★★★★★on Google 100+ students trained- Duration
- 45 Days
- Mode
- Live Online
- Batches
- Weekday & Weekend
- Hands-on
- End-to-end capstone
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About GCP Data Engineer Online Training
Data engineering on Google Cloud has moved past moving files into tables. Today a GCP data engineer is expected to load and model data in BigQuery, orchestrate pipelines with Airflow, and hand clean data to machine learning and Gen AI applications on Vertex AI. Our GCP Data Engineer training in Hyderabad covers that full path, in live online classes you can join from anywhere.
Course highlights
- BigQuery in depthLoading, partitioning, advanced SQL, MERGE, incremental loads, cost tuning and data governance.
- Airflow and Cloud ComposerBuild DAGs locally, then run production-style ELT and ML pipelines on Cloud Composer.
- Vertex AI and GeminiTraining, pipelines, endpoints, model monitoring, and a RAG application with Gemini and vector search.
- End-to-end capstoneCloud Storage to Composer to BigQuery to Vertex AI and back, with a Gemini RAG app on the same data.
Skills you’ll learn
What You Will Be Able to Build
By the end of the course you will have built a working data and AI platform on GCP: files land in Cloud Storage, Cloud Composer cleans them and loads them into BigQuery, a Vertex AI pipeline trains, evaluates and deploys a model, batch predictions are written back to BigQuery, and a Gemini-based RAG app answers questions on the same data.
Along the way you learn the parts interviewers ask about most: BigQuery cost and performance, partitioned and clustered tables, incremental loads with MERGE, DAG design, retries and failure handling, and how to monitor models for drift.
Why GCP Data and AI Skills Are in Demand
BigQuery is one of the most widely used cloud data warehouses, and companies using it are now adding AI on top of the same data. People who can handle both the data pipeline and the ML or Gen AI side are rare, which is what this GCP data engineering course is designed for.
Who Should Join This GCP Data Engineer Course?
The course starts with GCP basics, so you do not need Google Cloud experience. These groups do well:
SQL and ETL Developers
Developers with SQL, Informatica, SSIS or similar experience who want to move to cloud data engineering.
Data Analysts
Analysts who already write SQL and want to build pipelines and work with ML and AI on the same data.
Data Engineers on Other Clouds
Engineers from AWS or Azure data platforms adding Google Cloud to their profile.
Python Developers and Freshers
Developers and graduates with Python and SQL basics who want a data and AI engineering role.
Prerequisites for GCP Data Engineer Training
These will help you get the most from the course:
- Working knowledge of SQL: SELECT, joins, GROUP BY
- Basic Python — reading and writing simple scripts
- A general idea of what a database and a data pipeline are
- A Google Cloud free-trial account for labs (we help you set it up)
GCP Data Engineer Course Syllabus
The 45-day syllabus runs in 13 modules: Google Cloud basics, BigQuery from the basics to advanced SQL, the machine learning workflow, Vertex AI and Vertex AI Pipelines, Generative AI with Gemini, Apache Airflow, Cloud Composer, orchestrating BigQuery and Vertex AI from Airflow, MLOps on GCP, and an end-to-end capstone project. Most modules end with a lab.
- GCP projects, billing and IAM
- Service accounts and permissions
- Cloud Storage buckets
- Google Cloud Console vs gcloud CLI
- Networking basics for data and AI workloads
- Lab: Create a project, storage bucket, service account and IAM roles
- Datasets, tables and schemas
- Loading CSV, JSON and Parquet files
- SQL basics in BigQuery
- Partitioned and clustered tables
- Views and materialized views
- Query cost and performance tuning
- Lab: Load data from Cloud Storage into BigQuery and run analytical queries
- Nested and repeated fields
- Window functions and advanced SQL
- MERGE statements and incremental loads
- External tables
- Scheduled queries
- Permissions and data governance in BigQuery
- Project: Build a small analytics warehouse from raw files in Cloud Storage
- Machine learning life cycle
- Training vs inference
- Feature engineering
- Train, validation and test datasets
- Evaluating models
- Batch vs online predictions
- Lab: Prepare a training dataset from BigQuery and train a model
- Vertex AI architecture
- Vertex AI Workbench and notebooks
- Custom training jobs and prebuilt containers
- Model Registry
- Deploying models to Vertex AI endpoints
- Lab: Train a model and deploy it to an endpoint
- Kubeflow Pipelines concepts and pipeline components
- Passing artifacts between pipeline steps
- Training and evaluation pipelines
- Registering and deploying models from a pipeline
- Lab flow: BigQuery → Preprocess → Train → Evaluate → Register → Deploy
- Gemini models and prompting
- Structured outputs
- Embeddings, vector search and RAG
- Evaluating generative models
- Safety and grounding
- Project: Enterprise RAG application on GCP data
- DAGs, tasks and dependencies
- Operators and XCom
- Variables and connections
- Scheduling, retries and failure handling
- Lab: Build a multi-step Airflow DAG on a local machine
- Cloud Composer architecture
- Creating a Composer environment
- Deploying DAGs
- GCP operators
- Service accounts and IAM for Composer
- Logging, monitoring and troubleshooting Composer environments
- Running BigQuery SQL from Airflow
- Creating and loading tables
- Data quality checks
- Incremental pipelines and dataset dependencies
- Failure and retry strategy
- Lab: Production-style ELT pipeline
- Starting Vertex AI training jobs
- Triggering Vertex AI pipelines
- Monitoring jobs and registering models
- Running batch predictions
- Conditional model deployment
- CI/CD for DAGs and pipelines with Cloud Build and GitHub
- Model monitoring for data drift and prediction skew
- Scheduled retraining from Cloud Composer
- Model versions and rollback with Model Registry
- Logging, alerts and cost monitoring with Cloud Monitoring
- End-to-end data and AI platform: Files land in Cloud Storage, Cloud Composer cleans and loads them into BigQuery, a Vertex AI pipeline trains, evaluates and deploys a model, batch predictions go back to BigQuery, and a Gemini RAG app answers questions on the same data
GCP Data Engineer Projects and Labs
Labs run through every module, and the larger projects bring them together.
Analytics Warehouse in BigQuery
Build a small analytics warehouse from raw files in Cloud Storage, with partitioned tables and incremental loads.
Production-Style ELT Pipeline
An Airflow DAG on Cloud Composer that loads BigQuery tables with data quality checks, dependencies and retries.
Enterprise RAG Application
A Gemini-based RAG app on Vertex AI that answers questions from your own GCP data using embeddings and vector search.
End-to-End Capstone
Cloud Storage to Composer to BigQuery to a Vertex AI training pipeline, batch predictions back to BigQuery, and the RAG app on top.
GCP Data Engineering With AI
This course treats AI as part of the data engineer's job, not a separate topic. You prepare training data in BigQuery, train and deploy models on Vertex AI, run pipelines from Airflow, and monitor models for drift. On the Gen AI side you work with Gemini models, structured outputs, embeddings, vector search and grounding to build a RAG application, and you learn how to evaluate its answers.
Career Opportunities After GCP Data Engineer Training
Typical roles for people with GCP data and AI skills:
The course helps you prepare for the Google Cloud Professional Data Engineer certification and for interviews. Our placement team supports you with resume help, mock interviews and referrals.
GCP Data Engineer Course Fee and Batches
The GCP Data Engineer course fee depends on the batch type and current offers. Call us on +91 90327 34343 or message us on WhatsApp for the exact fee and the next batch date. EMI is available.
All batches are live online with weekday and weekend options. Every class is recorded, so you can catch up if you miss one, and students joining from the USA, UK or Canada get time slots that suit their time zone.
Why Learn GCP Data Engineering at VR IT Solutions?
Data and AI in One Course
You learn the pipeline side and the ML and Gen AI side together, on the same data, the way modern data teams work.
Labs on Your Own GCP Project
Every lab runs in your own Google Cloud project, so you leave with working pipelines and a capstone you can show.
Live Online With Recordings
Live classes, recordings of every session, and backup classes when needed.
Interview and Job Support
Resume help, mock interviews and placement assistance after the course.
From your first class to your first project at work
We stay with you after the course ends — through interviews, placement, and your first months on the job.
- Free demo classMeet the trainer and see how classes run before you pay.
- Live trainingLive online classes, with hands-on practice every session.
- Real-time projectWork through a real implementation scenario from start to finish.
- Course certificateGet your course completion certificate from VR IT Solutions.
- Resume & mock interviewsResume review and practice interviews with experienced professionals.
- Placement assistanceInterview referrals and guidance until you get placed.
- Job supportStuck on a task in your new job? Our trainers help you solve it.
What our students say on Google
G4.7★★★★★See all reviews on GoogleHi I'm Dasararh
I have Join VRIT Solutions to transform my future. VRIT is the right place to sharpen your Salesforce training skills while gaining cutting-edge experience through real time examples and live projects. Faculty covered in real time examples and practical oriented I joined this course in 2025 and now working in a reputed healthcare company.I would strongly recommended to chose VR IT Solutions for Salesforce online training .
Thanks again for VR IT .
One of the top institutes for training, with faculty member Ashok Kumar being highly knowledgeable in the subject.
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The course covered Base SAS, SQL, Macros, and SDTM/ADaM mapping basics, especially for advanced topics like SAS Macros, making complex concepts easy to understand. which are extremely valuable for anyone aiming to build a career in Clinical SAS programming.
I highly recommend VR IT Solutions to anyone looking to build or advance their SAS career.
Preparing for GCP Data Engineer interviews?
Read free interview questions and real-time scenarios with answers. Our students get all 6+ in the Job Ready Hub.
Batch timings
Pick a slot that fits around college or work. New batches start every month.
Book your slot- Morning7 – 9 AM
- Afternoon12 – 2 PM
- Evening6 – 8 PM
- WeekendSat & Sun
- Fast trackOn request
- One-to-one training
- Backup classes
- Free resume preparation
- Mock interviews
- Job support
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Get your course completion certificate
- Issued by VR IT Solutions when you complete the course and the real-time project
- Every certificate has its own certificate ID
- Add it to your resume and LinkedIn profile
GCP Data Engineer Online Training in USA, UK & Canada
Looking for GCP Data Engineer online training from the USA, UK or Canada? Join our live online batches from anywhere — classes run in time slots that suit your time zone, every session is recorded for revision, and you get the same real-time project, certificate and placement support as our Hyderabad students.
- USAEST & PST evening slots
- UKGMT / BST slots
- CanadaEST & CST slots
- WeekendSat & Sun, all time zones
Visit our Ameerpet centre
- 506/A, Aditya Enclave, Nilagiri Block, 5th Floor, Ameerpet, Hyderabad – 500016
- Monday to Saturday, 9 AM – 7 PM
- +91 90327 34343
- info@vritsol.com
GCP Data Engineer Training FAQs | VR IT Solutions
A GCP data engineer builds pipelines that bring data into Google Cloud, models it in BigQuery, schedules and monitors those pipelines, and prepares data for analytics, machine learning and Gen AI applications.
It is live online only, with weekday and weekend batches. Every session is recorded.
The course runs for 45 days in 13 modules, ending with an end-to-end capstone project.
Yes. The topics map to the main areas of the certification, and we guide you on exam preparation.
No. We explain the ML workflow from the basics. The focus is on preparing data, running pipelines and deploying and monitoring models on Vertex AI.
Yes. You work with Gemini models, embeddings, vector search and RAG on Vertex AI, and build a RAG application on your own data.
Absolutely. VR IT Solutions offers free demo sessions for all courses. You can attend a live class, interact with the trainer, and get a feel for the training quality before making any commitment. Just reach out to our team and we will schedule one for you.
We provide end-to-end placement assistance — from building a job-ready resume to conducting mock interviews with industry experts. Our team actively shares job openings, refers students to hiring companies, and supports you until you land your first role.
Yes. Every trainer at VR IT Solutions has between 8 and 13 years of hands-on industry experience. They have worked on real enterprise projects and bring that practical knowledge directly into the classroom, which makes a significant difference when you face real job interviews.
Course Training
Industry-certified · Real-time projects · 100% Placement Support
Call us: +91-9032734343