Data Engineering – AWS Course Overview
Build a Job-Ready Career in Data Engineering with AWS
Our Data Engineering – AWS course is designed to help learners
understand how modern organizations collect, process, transform,
store and analyse large volumes of data using cloud technologies.
You will gain practical knowledge of Python, SQL, AWS cloud
services, ETL and ELT pipelines, data lakes, data warehouses,
Apache Spark and PySpark.
The training focuses on hands-on learning so that you can build
real-world data engineering projects and understand how different
AWS services work together to create scalable data solutions.
What You Will Learn
Develop practical skills required to design, build and maintain
modern AWS-based data engineering solutions.
Python Programming for Data Engineering
Advanced SQL & Database Concepts
AWS Cloud Fundamentals
Amazon S3 & Cloud Storage
Data Lake Architecture
ETL & ELT Pipeline Development
AWS Glue
Amazon Athena
Amazon Redshift
AWS Lambda
Apache Spark & PySpark
Real-World AWS Data Projects
Data Engineering – AWS Course Syllabus
10 Modules
80 Hours Total
12 Projects
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Introduction to Data Engineering and Data Engineer Roles
1 hr
-
Data Engineering Lifecycle and Architecture
1 hr
-
Structured, Semi-Structured and Unstructured Data
1 hr
-
OLTP vs OLAP Systems
1.5 hrs
-
Data Pipelines, ETL and ELT Concepts
1.5 hrs
-
Project: Design a Modern Data Engineering Architecture
2 hrs
-
Python Fundamentals for Data Engineering
1 hr
-
Functions, Modules and Exception Handling
1 hr
-
File Handling and Data Processing
1 hr
-
Working with CSV, JSON and Parquet
1.5 hrs
-
Python Libraries for Data Engineering
1.5 hrs
-
Project: Build a Python Data Processing Pipeline
2 hrs
-
Relational Database Concepts
1 hr
-
SELECT, Filtering, Sorting and Aggregation
1 hr
-
GROUP BY and HAVING
1 hr
-
JOINs, Subqueries and CTEs
1.5 hrs
-
Window Functions
1.5 hrs
-
Project: Build a Business Analytics Database
2 hrs
-
Introduction to AWS Cloud
1 hr
-
AWS Global Infrastructure
1 hr
-
IAM Users, Roles and Policies
1.5 hrs
-
AWS Security Fundamentals
1 hr
-
AWS Monitoring and Cost Management
1.5 hrs
-
Project: Configure a Secure AWS Environment
2 hrs
-
Amazon S3 Fundamentals
1 hr
-
S3 Buckets, Objects and Permissions
1 hr
-
S3 Storage Classes and Lifecycle Policies
1 hr
-
Data Lake Architecture
1.5 hrs
-
Partitioning and Parquet Files
1.5 hrs
-
Project: Build an AWS S3 Data Lake
2 hrs
-
Introduction to AWS Glue
1 hr
-
Glue Data Catalog
1.5 hrs
-
Glue Crawlers
1 hr
-
Glue ETL Jobs
2 hrs
-
PySpark Transformations
1.5 hrs
-
Project: Build an Automated AWS Glue ETL Pipeline
2 hrs
-
Introduction to Amazon Athena
1 hr
-
Query S3 Data using SQL
1.5 hrs
-
External Tables and Schemas
1 hr
-
Partitioning and Query Optimisation
1.5 hrs
-
Project: Build a Serverless Analytics Solution
2 hrs
-
Introduction to Amazon Redshift
1 hr
-
Data Warehouse Architecture
1.5 hrs
-
Schemas, Tables and Data Loading
1.5 hrs
-
Analytical SQL Queries
1.5 hrs
-
Performance Optimisation
1.5 hrs
-
Project: Build an AWS Cloud Data Warehouse
2 hrs
-
Introduction to Apache Spark
1 hr
-
Spark Architecture
1.5 hrs
-
DataFrames and Transformations
1.5 hrs
-
PySpark Data Processing
2 hrs
-
Project: Process Large Datasets using PySpark
2 hrs
-
End-to-End AWS Data Engineering Architecture
1 hr
-
Data Ingestion and Validation
1 hr
-
ETL Pipeline Development
1.5 hrs
-
Data Lake & Warehouse Integration
1 hr
-
Monitoring and Deployment
1 hr
-
Interview Preparation & Mock Interviews
1.5 hrs
Who Should Attend
This course is suitable for students, graduates, working
professionals and career switchers who want to build practical
skills in AWS Cloud and Data Engineering.
Freshers & Students
Graduates and students looking to start their careers
in Data Engineering, Cloud Computing and AWS.
Career Switchers
Professionals looking to transition into Data Engineering
and AWS Cloud roles.
Data Professionals
SQL developers, data analysts and database professionals
who want to develop cloud data engineering skills.
AWS Enthusiasts
Learners interested in AWS, cloud architecture,
data lakes, ETL and big data processing.
Tools & Technologies Covered
Work with modern technologies used in cloud-based data
engineering environments.
AWS Data Engineering Certification Path
Build your foundation in AWS Cloud and Data Engineering and
prepare for relevant AWS certification pathways based on your
experience and career goals.
Course Completion Certificate
Receive the Procode Technologies Data Engineering
certificate after completing the course requirements.
AWS Cloud Foundation
Strengthen your understanding of AWS services,
cloud architecture, security and core concepts.
AWS Data Engineering Certification Preparation
Prepare for AWS data engineering certification pathways
covering data ingestion, transformation, storage,
security and pipelines.
Cloud Data Engineering Career
Continue developing skills in cloud architecture,
big data, analytics and modern data platforms.
