Build practical expertise with GCP Data Engineering Training at Raise Tech. Learn to design, build, and manage modern data pipelines using Google Cloud, BigQuery, Dataflow, Pub/Sub, and Cloud Composer. Along with core data engineering skills, explore AI-powered analytics, BigQuery ML, Vertex AI, and real-world cloud projects. With hands-on learning and career-focused guidance, Raise Tech helps you develop the skills needed to become job-ready for modern data engineering roles.
Students Trained
Placement Assistance
Project Based
Mentor Sessions
Python and advanced SQL - the daily muscle of every data engineer.
Data modeling, warehousing, and modern ELT patterns.
BigQuery deeply: partitioning, clustering, performance, and cost control.
Batch and streaming pipelines with Dataflow (Apache Beam) and Pub/Sub.
Orchestration with Cloud Composer (Airflow) and dbt on BigQuery.
AI on the warehouse: Vertex AI, BigQuery ML, and LLM-powered analytics.
Every skill is practiced through builds – not slides.
→ Python for data engineering
→ Advanced SQL & window functions
→ Linux, Git, and GCP basics
→ Data modeling (Kimball + modern)
→ BigQuery internals & cost control
→ Partitioning, clustering, and performance
Next, build practical batch and streaming pipelines using modern GCP services.
→ Batch ELT with dbt on BigQuery
→ Streaming with Pub/Sub + Dataflow
→ Schema evolution & data quality
From there, learn how to manage reliable workflows and production-ready data platforms.
→ Airflow / Cloud Composer DAGs
→ CI/CD for data pipelines
→ Monitoring, alerting, and lineage
In addition, bring AI into your data workflows with modern Google Cloud tools.
→ BigQuery ML for in-warehouse ML
→ Vertex AI pipelines & endpoints
→ LLM-powered analytics & RAG over data
Finally, apply your skills through an end-to-end GCP data platform and prepare for your next career opportunity.
→ End-to-end GCP data platform
→ GCP Data Engineer certification preparation
→ Interview preparation + referrals
First, ingest raw data, transform it with dbt, and build trusted data marts on BigQuery.
Next, create a Pub/Sub and Dataflow pipeline that processes events and delivers data to BigQuery in real time.
After that, use Airflow and Cloud Composer to schedule, monitor, and manage production-ready data workflows.
Finally, combine BigQuery ML with an LLM interface so users can explore and query data using natural language
No. We start with GCP fundamentals and ramp up to production patterns.
Yes – content closely tracks the Professional Data Engineer exam.
Yes. Every phase ships running pipelines, not just slides.
Limited seats. Real mentorship. Real outcomes.
