Data Engineering Foundations on AWS Certified Course

The Data Engineering Foundations on AWS – Certified Course is designed to equip learners with the core skills required to build, manage, and optimize modern data pipelines on the Amazon Web Services platform. This program covers essential data engineering concepts including data ingestion, storage, transformation, orchestration, and analytics using AWS services such as S3, Glue, Redshift, Kinesis, Lambda, and EMR. Learners gain hands-on practice in designing scalable architectures, implementing ETL workflows, working with big data ecosystems, and applying best practices for security, cost optimization, and performance tuning. By the end of this course, students will be able to develop end-to-end cloud data engineering solutions aligned with industry standards and AWS architectural principles.

Skills You Will Gain:

• Understanding of AWS cloud architecture for data engineering
• Building scalable data pipelines on AWS
• Data ingestion from various sources (batch & streaming)
• Transforming and preparing data for analytics and AI
• Big data processing using AWS EMR and Spark
• Creating and managing data lakes on Amazon S3
• Designing data warehouses using Amazon Redshift

The Course Content Enables Students To:

• Understand fundamental principles of data engineering on AWS
• Design and implement scalable data pipelines for cloud environments
• Ingest, transform, and prepare data for analytics and AI/ML
• Create and manage data lakes using Amazon S3
• Build and optimize data warehouses using Amazon Redshift
• Process big data using EMR and Spark frameworks

Syllabus:

Module 1: Introduction to Data Engineering on AWS

• Overview of AWS data ecosystem
• Cloud computing & architecture fundamentals
• IAM, security, and access management basics
• Core AWS services for data engineering

Module 2: AWS Storage Fundamentals

• Amazon S3 fundamentals
• Storage classes & lifecycle policies
• Building data lakes on S3
• Best practices for secure and scalable storage

Module 3: Data Ingestion Techniques

• Batch vs streaming ingestion
• Using AWS Glue, Lambda, and Kinesis
• Data extraction from APIs and databases
• Automating ingestion workflows

Module 4: Data Transformation and ETL

• ETL & ELT concepts
• Transforming data using AWS Glue and PySpark
• Data cleaning and enrichment
• Workflow orchestration with Step Functions

Module 5: Data Warehousing

• Amazon Redshift architecture
• Table design & optimization
• Querying & analytics
• Redshift Spectrum & integration with S3

Module 6: Big Data Processing

• Distributed data processing using EMR
• Spark and PySpark workflows
• Handling large-scale datasets
• Optimization for performance and cost

Module 7: Serverless Data Engineering

• Lambda for serverless transformations
• Event-driven pipelines
• Orchestration with Step Functions
• Integration with S3, DynamoDB, and Redshift

Module 8: Real-Time Data Pipelines

• Amazon Kinesis Streams & Firehose
• Event-driven data processing
• Real-time analytics & dashboards
• Monitoring and error handling

Module 9: Data Governance and Security

• Data cataloging with AWS Glue
• Data encryption and KMS
• Monitoring pipelines using CloudWatch
• Ensuring data quality, lineage, and compliance

Module 10: Capstone Project

• End-to-end AWS data pipeline
• Data ingestion → Transformation → Storage → Analytics
• Documentation and presentation
• Industry-relevant scenario implementation

Tools Covered:

• Amazon S3 – Object storage & data lakes
• AWS Glue – Serverless ETL & data transformation
• Amazon Redshift – Data warehousing & analytics
• Amazon EMR – Big data processing with Spark/Hadoop
• AWS Lambda – Serverless compute for ETL pipelines

Live Projects:

• Data Lake Creation Project
• ETL Pipeline Project
• Data Warehousing Project
• Real-Time Streaming Analytics Project

Who Is This Program For?

• Aspiring Data Engineers looking to start their career in cloud-based data pipelines
• Cloud Engineers wanting hands-on experience with AWS data services
• Software Developers transitioning into data engineering roles
• Analytics & BI Professionals aiming to work with large-scale data
• Students in Computer Science, IT, or Engineering preparing for AWS certifications

Career Opportunities:

• AWS Data Engineer – Design, build, and manage scalable data pipelines
• Big Data Engineer – Work on distributed data processing using AWS EMR and Spark
• Cloud Data Architect – Plan and implement cloud-based data solutions
• ETL Developer – Build and optimize ETL workflows for analytics and AI
• Business Intelligence (BI) Engineer – Transform and model data for reporting and dashboards

How To Apply:

• Mobile: 9100348679
• Email: coursedivine@gmail.com