AWS Data Engineering
S3-based lakes, Glue, Athena, Redshift and orchestration patterns for analytics workloads on AWS.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Mon, 04 Jan 2027 · 40 hours · 5 days
AWS Data Engineering
Duration: 40 hours · 5 days
Next batch: Mon, 04 Jan 2027
Format: Online live · Bengaluru classroom
Modules: 9 · Topics: 42
Level: Intermediate
Fee: ₹17,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| AWS Data Engineering | Mon, 04 Jan 2027 | Online live | Weekday | 5 days | Enquire |
| AWS Data Engineering | Sat, 27 Feb 2027 | Online live | Weekend (Sat) | 6 Saturdays | Enquire |
Soaring demand, and where it takes you
AWS is widely used by Indian startups and enterprises alike, keeping AWS data engineering skills in steady demand.
Become an expert
Roles this programme prepares you for:
Become a AWS Data Engineer
Builds lakes, ETL and warehouses on AWS.
- Design an S3-based data lake
- Catalogue and transform data with AWS Glue
- Query data in place with Athena
About the course
Build analytics data platforms on AWS: S3 data lakes, AWS Glue for cataloguing and ETL, Athena for querying, Redshift for warehousing, orchestration options, security with IAM and Lake Formation, and cost control.
S3-based lakes, Glue, Athena, Redshift and orchestration patterns for analytics workloads on AWS.
Why should I attend?
Data engineers on AWS
Developers moving into data
ETL developers migrating to cloud
Platform teams
Prerequisites
SQL; Python basics; AWS account basics helpful
Level
Intermediate
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 40 hours · 5 days
Course fee (open batch): ₹17,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Design an S3-based data lake
- Catalogue and transform data with AWS Glue
- Query data in place with Athena
- Load and model data in Redshift
- Orchestrate pipelines on AWS
- Secure data with IAM and Lake Formation
Skills you will gain
Storage and catalogue
Processing
Security and ops
Tools and platforms covered
Apache Airflow
PythonCourse curriculum
M01 AWS analytics overview
- Core services
- Reference architectures
- Cost model
- Data lake vs lakehouse on AWS
- Account structure
🧪 Hands-on: Explore a reference design
M02 S3 data lakes
- Zones and layout
- File formats
- Partitioning
- Lifecycle and storage classes
- Open table formats (Iceberg)
🧪 Hands-on: Build a lake layout
M03 Glue
- Data Catalog and crawlers
- Glue ETL jobs
- Job bookmarks
- Glue Studio
- Data quality rules
🧪 Hands-on: Catalogue and transform data
M04 Querying and warehousing
- Athena
- Redshift basics
- Loading and modelling
- Redshift Serverless
- Materialised views
🧪 Hands-on: Query with Athena and load Redshift
M05 Orchestration
- Step Functions and managed Airflow
- Event-driven with Lambda
- Scheduling
- EventBridge
- Error handling
🧪 Hands-on: Orchestrate the pipeline
M06 Security and cost
- IAM
- Lake Formation
- Cost optimisation
- Encryption with KMS
- Cost allocation tags
🧪 Hands-on: Lock down and optimise
M07 Streaming on AWS
- Kinesis Data Streams
- Firehose
- Amazon MSK overview
- Real-time analytics
🧪 Hands-on: Stream events into S3 and query them with Athena
M08 Infrastructure as code for data
- CloudFormation/CDK basics
- Deploying Glue jobs
- Environments
- CI/CD
🧪 Hands-on: Deploy a Glue pipeline with code
M09 Capstone: AWS lakehouse
- Ingest
- Catalogue
- Transform
- Query and govern
🧪 Hands-on: Build a governed S3 lakehouse queried by Athena and Redshift
Hands-on projects
AWS data lake
An S3 lake catalogued with Glue, queried with Athena and feeding Redshift.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for AWS Data Engineering. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Is AWS free tier enough?
Some labs fit within free-tier limits; we provide guidance to avoid unexpected costs.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹17,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
40 hours · 5 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
Build analytics data platforms on AWS: S3 data lakes, AWS Glue for cataloguing and ETL, Athena for querying, Redshift for warehousing, orchestration options, security with IAM and Lake Formation, and cost control.
What is AWS Data Engineering?
S3-based lakes, Glue, Athena, Redshift and orchestration patterns for analytics workloads on AWS.
Who this course is for
Data engineers on AWS, Developers moving into data, ETL developers migrating to cloud, Platform teams.
What you will be able to do
Design an S3-based data lake. Catalogue and transform data with AWS Glue. Query data in place with Athena. Load and model data in Redshift. Orchestrate pipelines on AWS. Secure data with IAM and Lake Formation.
Career paths
Roles this course supports include AWS Data Engineer, Cloud Analytics Engineer, Data Platform Engineer. AWS is widely used by Indian startups and enterprises alike, keeping AWS data engineering skills in steady demand.
Cloud guides
AWS vs Azure vs Google Cloud: Which Should Your Team Learn?
The three largest public clouds share core concepts but differ in strengths and ecosystem.
Serverless vs Containers
Two popular ways to run applications in the cloud, with different trade-offs.
AWS Certifications Explained
An overview of AWS certification levels and how to choose.
What Is AWS IAM?
AWS Identity and Access Management controls who can do what in an AWS account.