GCP Data Engineering Training Program
SQL • Python • GCP • BigQuery • Dataflow • Pub/Sub • Airflow
A structured 12-week program designed around core data engineering concepts, SQL, Python, Google Cloud Platform services, data warehousing, ETL/ELT, distributed processing, streaming, orchestration, data quality, monitoring and enterprise-style projects.
What You Will Master in 12 Weeks
A structured 12-week program designed around core data engineering concepts, SQL, Python, Google Cloud Platform services, data warehousing, ETL/ELT, distributed processing, streaming, orchestration, data quality, monitoring and enterprise-style projects.
Program Highlights
Daily & Weekly Learning Routine
- Daily 90-minute structured classes with enterprise cloud live demos
- Daily practice exercises with automated SQL and Python data validation
- Weekly Show & Tell architecture defenses
- Oral sessions, presentations, and mock technical interviews every Friday
Technologies & Core Competencies
Targeted industry stack covered through live coding, architecture blueprints, and dedicated sandbox labs.
Core Competencies Covered
Enterprise Tools & Platforms
Comprehensive 12-Week Curriculum
Exact, comprehensive weekly topics taken directly from official CES program specifications. Every week features hands-on assignments and code reviews.
Data Engineering Fundamentals + SQL
Architecture diagrams, data storage paradigms, and relational querying fundamentals
- What is Data Engineering?
- OLTP vs OLAP
- ETL vs ELT
- Data Lake, Warehouse, Lakehouse
- Batch vs Streaming
- BigQuery overview and features
- +3 more topics
SQL Business Analytics Assignment (Complex analytical queries)
Advanced SQL & Analytical Functions
Mastering window functions and time-series analytical calculations for reporting
- Window functions
- LAG, LEAD
- Running totals, moving averages
- Date/time functions
- String functions
- Summary, Logical, Search, Conditional functions
- +1 more topics
Advanced SQL Benchmark Challenge
Python Fundamentals & Data Validations
Core Python data structures, file processing, and defensive validation logic
- Python syntax, variables, data types, conditions, loops
- Functions, modules, packages, exceptions
- Lists, dictionaries, tuples, sets, comprehensions
- File processing: CSV, JSON, Excel
- Libraries
- Data Validations: Null, Duplicate, Data type, Error logging
Automated Data Validation Engine Mini Project
Python for Data Engineering
Pandas data transformations, API ingestion, database drivers, and modular frameworks
- Pandas + data transformation
- REST API integration
- Database connectivity
- Logging, configuration, exception handling
- Reusable ETL framework
Reusable Modular ETL Framework
GCP Fundamentals & Storage Architecture
Cloud infrastructure, IAM security, service accounts, and Google Cloud Storage lifecycles
- GCP architecture, projects, regions, zones, billing
- IAM: users, groups, roles, service accounts
- GCS buckets, objects, storage classes, lifecycle management
- GCP CLI + Python SDK
Automated GCS Bucket Lifecycle & Python Storage Driver
BigQuery Advanced & Query Optimization
Columnar storage mechanics, complex nested data, and high-performance querying
- BigQuery architecture, datasets, tables, views
- Loading CSV/JSON/Parquet from GCS
- Advanced BigQuery SQL: ARRAY, STRUCT, UNNEST
- Partitioning, clustering, query optimization and cost control
Optimized Partitioned & Clustered BigQuery Warehouse
Data Warehouse & Data Modelling
Enterprise data warehousing layers, dimensional modeling, and slowly changing dimensions
- Data warehouse architecture: staging, integration, presentation layers
- Fact and dimension tables, star schema design
- Slowly Changing Dimensions: SCD Type 1 & Type 2
- Build Sales Data Warehouse
Enterprise Sales Data Warehouse Build
ETL / ELT + Incremental Processing
Incremental loading patterns, MERGE operations, and Change Data Capture (CDC)
- Full load vs incremental load strategies
- MERGE and UPSERT operations
- Watermarks and tracking high-water marks
- CDC concepts and change handling
- End-to-end incremental pipeline using Python + GCS + BigQuery
End-to-End Incremental Pipeline (Python + GCS + BigQuery)
Apache Beam + Dataflow
Serverless distributed data processing using Apache Beam unified programming model
- Distributed processing concepts
- Beam pipeline architecture, PCollection, ParDo, DoFn
- Dataflow architecture and serverless execution
- Build Dataflow pipeline
Dataflow Distributed Pipeline Build
Pub/Sub + Real-Time Data Engineering
Streaming architecture, event-driven message queuing, and real-time processing
- Batch vs streaming paradigms
- Events, messages, topics, subscriptions, producers, consumers
- Pub/Sub → Dataflow streaming ingestion
- Real-time analytics project
Real-Time Streaming Analytics Project
Airflow + Data Quality + Monitoring
Workflow orchestration, DAG scheduling, automated quality tests, and failure alerting
- Airflow overview and fundamentals
- Data pipeline orchestration with DAGs
- Automated data quality checks
- Monitoring, logging, and troubleshooting failures
Automated Airflow Orchestration DAG Suite
Enterprise Capstones & Defense
Building and defending end-to-end data platform pipelines with all 12 deliverables
- Enterprise Sales pipeline
- Logistics real-time tracking
- Incentives calculation
- Healthcare analytics
- Project defense and technical interview preparation
Final 12-Deliverable Enterprise Project Defense
Industry Projects & Capstone Deliverables
Graduation requires building and presenting an enterprise portfolio project following the standard software development lifecycle.
Enterprise Sales Analytics Pipeline (GCS → Dataflow → BigQuery)
End-to-end pipeline ingesting retail transactional logs into GCS, transforming via Apache Beam Dataflow, loading into a partitioned BigQuery star schema with SCD Type 2 tracking, and scheduled via Airflow.
Real-Time Logistics Telemetry Streaming Pipeline
Pub/Sub ingestion of GPS event streams, processed dynamically through Cloud Dataflow windowing transforms, streaming into BigQuery for real-time fleet ETA calculations.
Who Can Join This Program
- Data analysts and SQL developers wanting to transition into enterprise Cloud Data Engineering
- Fresh engineering graduates with basic Python and database intuition looking for cloud specialization
- Backend developers and ETL developers seeking Google Cloud Platform production-grade skills
Laptop & System Requirements
- Basic understanding of relational databases and SQL
- Basic programming fundamentals
- Stable broadband connection
Target Career Opportunities & Preparation
Target Job Roles
Interview Preparation Structure
- Weekly oral sessions explaining distributed computing concepts and pipeline architectures
- Advanced SQL query challenges (window functions, CTEs, performance optimization)
- Mock technical interview drills covering real-world ETL failure scenarios and debugging
- Portfolio defense of the 12 project deliverables
Questions About This Program
Do I need prior cloud experience before joining?
No prior cloud certification is needed. The course starts with fundamental SQL and Python before introducing Google Cloud Platform architecture in Week 5.
Will I learn both Batch and Streaming pipelines?
Yes. The curriculum specifically dedicates distinct weeks to both batch architectures (BigQuery, GCS) and real-time streaming architectures (Pub/Sub, Apache Beam Dataflow).
Ready to Begin the GCP Data Engineering Training Program?
Register your profile for skill baseline evaluation or speak directly with an academic advisor via WhatsApp to confirm the next batch schedule.