Multi-Cloud Data Engineering (Azure + AWS + GCP) Course in Hyderabad
Build portable data pipelines and compare data services across Azure, AWS and Google Cloud
The Multi-Cloud Data Engineering course teaches you to build data platforms with skills that transfer across Microsoft Azure, Amazon Web Services and Google Cloud. You start with SQL, Python, Linux and data modelling, then learn cloud-neutral tools: Apache Spark, Delta Lake and Apache Iceberg, Apache Airflow, dbt, Apache Kafka and Terraform. For each cloud you map the same building blocks to native services: object storage with Amazon S3, Azure Data Lake Storage Gen2 and Cloud Storage, ingestion with AWS Glue, Azure Data Factory and Dataflow, warehouses with Amazon Redshift, Azure Synapse or Fabric and BigQuery, and streaming with Kinesis, Event Hubs and Pub/Sub. You then build the same lakehouse pipeline on all three clouds, comparing security models, cost, performance and operations. The course suits graduates and professionals who want a broad data engineering profile; basic SQL helps and Python is taught from the basics. It is a skills-based course and is not tied to a single vendor exam. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Data Engineering Foundations
- •Data engineering roles, architectures and lifecycle
- •ETL versus ELT; batch versus streaming; lakehouse patterns
- •Evaluating platforms: cost, skills, security and lock-in
- •Lab: Draw a reference architecture and map it to three clouds
- •Outcome: Think about data platforms independent of vendor
SQL, Python & Linux
- •SQL joins, CTEs, window functions and query tuning basics
- •Python, pandas and file formats such as CSV, JSON and Parquet
- •Linux and shell basics for data work
- •Lab: Build a Python and SQL transformation script
- •Outcome: Use the core languages of data engineering
Cloud Fundamentals Across Azure, AWS & GCP
- •Service mapping table for compute, storage, identity and networking
- •Identity and access: IAM, Microsoft Entra ID and service accounts
- •Cost controls and budgets on each cloud
- •Lab: Create a project, role and budget on each cloud
- •Outcome: Navigate three clouds safely
Object Storage & Data Lake Design
- •Lake zone design: raw, curated and serving
- •S3, ADLS Gen2 and Cloud Storage features compared
- •Partitioning, file sizing, lifecycle and security
- •Lab: Create the same lake structure on each cloud
- •Outcome: Design portable lake layouts
Apache Spark & PySpark
- •Spark architecture and DataFrame API
- •Spark SQL, joins, window functions and file formats
- •Performance tuning and running Spark on Databricks, EMR, Dataproc or Fabric
- •Lab: Transform a large dataset in PySpark
- •Outcome: Process large data with Spark anywhere
Table Formats: Delta Lake & Apache Iceberg
- •Delta Lake and Apache Iceberg concepts and differences
- •MERGE, time travel, compaction and schema evolution
- •Catalogs and interoperability across engines
- •Lab: Build an incremental upsert pipeline with an open table format
- •Outcome: Build reliable lakehouse tables on any cloud
Cloud-Native Ingestion & ETL Services
- •Azure Data Factory pipelines and mapping data flows
- •AWS Glue jobs, crawlers and Data Catalog
- •Google Cloud Dataflow and Dataform overview
- •Lab: Build the same ingestion on each cloud and compare effort
- •Outcome: Pick the right managed ETL service
Cloud Data Warehouses
- •Warehouse architectures and modelling for each service
- •Loading, partitioning and distribution or clustering choices
- •Cost models: slots, nodes, serverless and capacity
- •Lab: Load a star schema into two warehouses and compare queries
- •Outcome: Select and tune a cloud warehouse
Streaming: Kafka, Kinesis, Event Hubs & Pub/Sub
- •Event streaming fundamentals and Apache Kafka KRaft
- •Kinesis, Event Hubs and Pub/Sub compared
- •Spark Structured Streaming and windowed aggregation
- •Lab: Stream events through Kafka and one native service
- •Outcome: Build real-time pipelines with portable concepts
Orchestration with Apache Airflow
- •Airflow 3 concepts and the TaskFlow API
- •Cloud provider packages and managed Airflow services
- •Scheduling, retries, alerts and assets
- •Lab: Orchestrate pipeline steps that call all three clouds
- •Outcome: Run dependable, scheduled pipelines
Transformation with dbt & Data Quality
- •dbt project structure, models, tests and documentation
- •Incremental models and CI checks
- •Data quality frameworks and contracts
- •Lab: Build a tested mart with dbt on a cloud warehouse
- •Outcome: Produce tested, documented analytics tables
Infrastructure as Code with Terraform
- •Terraform workflow, state and modules
- •Provisioning storage, compute and IAM on three clouds
- •Environment strategy and CI integration
- •Lab: Deploy a landing zone on two clouds with one codebase
- •Outcome: Provision data platforms repeatably
Security, Governance & Cost Management
- •Least privilege, encryption and secrets on each cloud
- •Catalogs, lineage and classification: Purview, Lake Formation and Dataplex overview
- •Cost allocation, budgets and optimisation
- •Lab: Apply role-based access and a cost alert on all clouds
- •Outcome: Build governed, cost-aware platforms
Capstone & Interview Preparation
- •Capstone: implement one lakehouse pipeline on three clouds and compare
- •Architecture decision record and cost comparison
- •Interview practice: SQL, Spark and system design
- •Outcome: A portfolio project and a clear multi-cloud decision framework
Tools Covered
Projects Included
- Same Lake on Three Clouds: Build a lake structure and ingestion on Azure, AWS and Google Cloud; deliverable is a comparison of effort, cost and features.
- Portable Spark Pipeline: Run one PySpark pipeline on two platforms with Delta tables; deliverable is the code and a performance comparison.
- Warehouse Face-Off: Load a star schema into two cloud warehouses and benchmark queries; deliverable is a tuning and cost report.
- Streaming Across Services: Stream events through Kafka and a native cloud service into a lake; deliverable is a working pipeline and latency notes.
- Capstone Multi-Cloud Platform: Orchestrate ingestion, dbt transformation and Terraform deployment on at least two clouds; deliverable is a GitHub repository and architecture decision record.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Multi-Cloud Data Engineering (Azure + AWS + GCP) course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Multi-Cloud Data Engineering (Azure + AWS + GCP).
Certificate of Completion
Multi-Cloud Data Engineering (Azure + AWS + GCP)
This certifies that
[Your Name]
has successfully completed the
Multi-Cloud Data Engineering (Azure + AWS + GCP)
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Multi-Cloud Data Engineering (Azure + AWS + GCP) course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Multi-Cloud Data Engineering (Azure + AWS + GCP) Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Data Engineer, Cloud Data Engineer, Big Data Engineer, Analytics Engineer; support is guidance with the job search, not a guarantee of a job.