Cloud Data Engineering Training & Internship Program

CLOUD DATA ENGINEERING

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About The Course

The Cloud Data Engineering Training & Internship Program is a comprehensive, beginner-to-advanced course designed to help you build, manage, and optimize scalable data pipelines on cloud platforms. This program focuses on real-world data engineering practices used by modern enterprises to process massive volumes of data efficiently and securely.

You’ll learn how raw data moves from multiple sources to analytics-ready systems using cloud-native tools, distributed processing frameworks, and data warehouses. The program emphasizes hands-on implementation, industry workflows, and internship-style project experience to make you job-ready.

Whether you aim to become a data engineer or want to strengthen your backend and cloud skills, this program equips you with production-level data engineering expertise.

Key points

Lessons of the Course

MODULE 1: DATA INGESTION & CLOUD ARCHITECTURE

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Cloud Foundations & Storage Patterns

Object storage (S3 / GCS / Azure Blob) vs. block storage and understanding the data lake concept
Focus: Data partitioning and file formats (Parquet vs. Avro vs. JSON)
Project: Build a serverless ingestion layer that triggers on file uploads and catalogs data

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Batch vs. Streaming Ingestion

Implementing change data capture (CDC) and event-driven architectures
Tools: Apache Kafka, AWS Kinesis, Google Pub/Sub
Project: Build a real-time log streamer handling 10k events per second into a cloud landing zone

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Security & Identity (IAM)

Principle of Least Privilege (PoLP) and encryption at rest and in transit

MODULE 2: THE DATA WAREHOUSE & MODERN TRANSFORMATION

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Cloud Data Warehousing (CDW)

Architecture of Snowflake, BigQuery, and Amazon Redshift
Focus: Separation of compute and storage, clustering keys
Project: Migrate a large on-prem SQL database to a clustered Snowflake warehouse

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The Medallion Architecture & dbt (data build tool)

Bronze (raw) → Silver (cleaned) → Gold (business-ready) data layers
Mastering dbt for version-controlled, SQL-based transformations
Project: Build a production-grade transformation pipeline with automated documentation and testing

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Advanced SQL & Query Optimization

Window functions, CTEs, and query execution plan analysis
Focus: Optimizing queries to reduce cloud costs

MODULE 3: ORCHESTRATION, RELIABILITY & DATAOPS

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Workflow Orchestration (The Conductor)

Directed Acyclic Graphs (DAGs) and task dependencies
Tools: Apache Airflow, Prefect, Mage
Project: Orchestrate a multi-stage data pipeline with retries, alerts, and failure notifications

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Data Quality & Observability

Implementing circuit breakers for data integrity
Testing for nulls, uniqueness, and schema drift
Tools: Great Expectations, dbt-tests, Monte Carlo (basics)

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Capstone: The End-to-End Cloud Data Factory

Build a fully automated pipeline: Ingest API data → Load to CDW → Transform with dbt → Orchestrate with Airflow → Deploy via CI/CD
Deliverable: Live dashboard and GitHub repository with a documented, tested, and deployed data product

TOOLS & LIBRARIES SUMMARY

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Tools & Libraries Summary

Cloud Platforms: AWS (S3, Lambda) / GCP (BigQuery, GCS)
Warehousing: Snowflake, BigQuery, Redshift
Transformation: dbt (Core), SQL, Spark (PySpark)
Orchestration: Apache Airflow, Prefect
Ingestion: Fivetran, Airbyte, Kafka

What Our Students Say

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Learning MERN Stack at Excendra was transformative. Practical projects, clear explanations, and industry-focused mentorship helped me develop strong full-stack skills and confidence to build professional applications.

Mehran Muzaffar Software Developer COMTECH GROUP

I completed Full Stack Development training at Excendra. Practical projects and clear teaching built my skills and confidence. With their support, I secured a job soon after the course and recommend Excendra for tech career.

Hurain Javaid Software Engineer Sun-Net Inc.