Data Engineer Using AWS

This course helps you become a proficient AWS Data Engineer by combining core AWS services with real-time projects and hands-on labs. You’ll learn to build ETL pipelines using AWS Glue, process big data using EMR and PySpark, stream real-time data with Kinesis, run SQL queries using Athena, and manage structured/unstructured data across S3, Redshift, and DynamoDB. The training also covers infrastructure automation, cost-effective storage, and secure data handling with IAM and encryption mechanisms.

You’ll gain practical experience by developing data engineering workflows, ingesting large-scale datasets, orchestrating jobs using Step Functions, and analyzing data with QuickSight dashboards.

Tech Stack Covered:

AWS Services & Data Tools:
1. Amazon S3 (Data Lake Foundation)

Object storage for structured/unstructured data

Versioning, lifecycle rules, encryption

2. AWS Glue (ETL Platform)

Serverless ETL for data extraction, transformation, and cataloging

Glue Crawlers, Glue Jobs (Python/Spark), Data Catalog

3. Amazon Redshift (Data Warehouse)

Fast, scalable analytics queries

COPY commands, Redshift Spectrum for querying S3

4. Amazon EMR (Big Data Platform)

Hadoop, Hive, Spark clusters for large-scale batch processing

PySpark and notebook-based pipeline development

5. AWS Lambda (Serverless Automation)

Event-driven triggers for ETL tasks and data clean-up

Integration with S3, DynamoDB, Kinesis

6. Amazon Kinesis (Streaming Data)

Real-time ingestion and processing

Integration with Firehose, Lambda, and S3

7. AWS Athena (Serverless SQL Analytics)

Query S3 data directly with ANSI SQL

Partitioned and compressed data access

8. AWS Step Functions (Orchestration)

Manage multi-step workflows and job sequences

Connect Lambda, Glue, and SNS for automated pipelines

9. DynamoDB (NoSQL Storage)

High-performance key-value and document store

Real-time lookups, metadata, or caching layers

10. AWS CloudWatch & CloudTrail

Monitor logs, set alarms, and audit API-level activity

11. AWS QuickSight (Data Visualization)

Create interactive dashboards and charts

Connects with Redshift, Athena, and S3

Tools & Languages:

SQL – Advanced joins, aggregations, and performance tuning

Python – Scripting for Lambda and Glue ETL

PySpark – Distributed data processing on EMR/Glue

AWS CLI & Boto3 SDK – Automation of AWS operations

Jupyter Notebook – PySpark notebook development

Git & GitHub – Version control and collaborative development

Apache Airflow (Optional Advanced Module) – DAG-based pipeline orchestration

Security & Optimization Concepts:

IAM (Role-based access control for services and users)

Data encryption at rest and in transit (SSE-S3, KMS)

Cost optimization with reserved instances and lifecycle policies

Budget alerts and usage monitoring