Cloud Data Engineering with AWS Glue, Azure Synapse & Google BigQuery

Executive Overview

Modern enterprises are powered by cloud-native data platforms that enable scalability, agility, and real-time analytics. This 5-day corporate training program focuses on Cloud Data Engineering using AWS Glue, Azure Synapse Analytics, and Google BigQuery — the three most advanced platforms for building cloud-based data pipelines and analytics solutions. Participants will gain hands-on experience in data ingestion, transformation, integration, and orchestration across these leading platforms. The course is designed to bridge business and technology, helping professionals architect and operationalize cloud data ecosystems that drive enterprise-wide intelligence.

Objectives of the Training

Understand the architecture and features of AWS Glue, Azure Synapse, and Google BigQuery.
Learn how to design and implement cloud-native data pipelines for ETL and ELT workloads.
Gain practical skills in automating data ingestion, transformation, and cataloging.
Explore cloud storage integration, data lake design, and performance optimization.
Learn best practices for security, governance, and cost management in cloud data engineering.

Prerequisites

Familiarity with SQL and data modeling concepts.
Basic understanding of cloud computing and distributed systems.
Experience with data analytics tools or ETL workflows is beneficial.

What You Will Learn

Data engineering principles and cloud architecture fundamentals.
Data ingestion, transformation, and cataloging using AWS Glue, Synapse, and BigQuery.
Building scalable ETL/ELT pipelines for analytics and machine learning.
Integration with cloud storage (S3, ADLS, GCS) and BI tools.
Managing security, monitoring, and optimization for cloud data workflows.
Real-world case studies on enterprise data modernization and hybrid architectures.

Target Audience

This course is designed for Data Engineers, Cloud Architects, BI Developers, and Data Analysts responsible for managing data pipelines, data warehouses, and analytics systems in cloud environments. It is also suitable for IT professionals transitioning from traditional ETL systems to modern cloud data platforms.

Detailed 5-Day Curriculum

Day 1 – Cloud Data Engineering Fundamentals (6 Hours)

Session 1: Introduction to Cloud Data Engineering – Evolution and Use Cases.
Session 2: Architecture Overview – Data Lakes, Data Warehouses, and Lakehouses.
Session 3: Comparing AWS Glue, Azure Synapse, and Google BigQuery Architectures.
Hands-on: Setting Up Cloud Accounts and Exploring Data Engineering Consoles.

Day 2 – AWS Glue: Serverless Data Integration and Transformation (6 Hours)

Session 1: AWS Glue Architecture – Crawlers, Jobs, and Data Catalogs.
Session 2: ETL with Glue Studio – Writing PySpark Jobs and Transformations.
Session 3: Integration with S3, Redshift, and Athena.
Workshop: Building a Data Pipeline to Extract, Transform, and Load Data into a Data Lake.

Day 3 – Azure Synapse Analytics: Unified Data Platform (6 Hours)

Session 1: Overview of Synapse Workspaces, Dedicated Pools, and Serverless SQL Pools.
Session 2: Data Integration using Synapse Pipelines and Linked Services.
Session 3: Orchestrating ETL and ELT Workflows with Synapse and Data Factory.
Hands-on: Designing and Executing a Synapse Pipeline with Data Transformation Logic.

Day 4 – Google BigQuery: Scalable Analytics and Data Warehousing (6 Hours)

Session 1: BigQuery Architecture – Serverless, Distributed Query Processing, and Storage.
Session 2: Working with Datasets, Tables, and SQL-Based Analytics.
Session 3: Integration with Dataflow, Dataproc, and Looker for Visualization.
Workshop: Building a Real-Time Analytics Solution using BigQuery and Pub/Sub.

Day 5 – Cross-Cloud Data Engineering and Capstone Project (6 Hours)

Session 1: Designing Hybrid and Multi-Cloud Data Architectures.
Session 2: Best Practices for Security, Monitoring, and FinOps in Cloud Data Pipelines.
Session 3: Capstone Project – Building an End-to-End Cross-Cloud Data Pipeline using Glue, Synapse, and BigQuery.
Panel Discussion: Future of Data Engineering – Lakehouse, Data Mesh, and AI-Driven Automation.

Capstone Project

Participants will design and implement an end-to-end cloud data engineering solution integrating AWS Glue, Azure Synapse, and Google BigQuery. The project will involve building a multi-stage data pipeline that ingests, transforms, and visualizes data from multiple sources. Teams will demonstrate scalability, performance tuning, and governance best practices in their solutions.

Future Trends in Cloud Data Engineering

Cloud Data Engineering is evolving toward serverless, AI-driven, and multi-cloud ecosystems. Emerging trends such as Data Mesh, Lakehouse architecture, and automated data lineage tracking are transforming how organizations handle data at scale. With tools like AWS Glue, Azure Synapse, and BigQuery continuing to evolve, enterprises are increasingly moving toward real-time analytics and predictive intelligence. Professionals who master these cloud platforms will play a pivotal role in driving data modernization and digital transformation initiatives.