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Data Engineering · Corporate & open batches

Data Engineering Foundations

Pipelines, warehouses, lakes and the modern data stack explained hands-on, from first principles to a working end-to-end pipeline.

  • ✓ 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, 14 Dec 2026 · 40 hours · 5 days

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Data Engineering

Data Engineering Foundations

Duration: 40 hours · 5 days
Next batch: Mon, 14 Dec 2026
Format: Online live · Bengaluru classroom
Modules: 9 · Topics: 42
Level: Beginner to Intermediate
Fee: ₹17,999 per participant + GST

Enquire now
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Learn from a practising trainer

Trusted by leading experts

Karthick Selvam
Founder · Data Engineering Corporate Trainer · Bengaluru
Hands-on delivery
Labs, reviews and a capstone in every programme
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Schedule

Upcoming batches

Open batches for individuals; corporate batches on your dates.

ProgrammeStartsModeScheduleDuration
Data Engineering FoundationsMon, 14 Dec 2026Bengaluru classroomWeekday
9:30 AM – 5:30 PM IST		5 daysEnquire
Data Engineering FoundationsSat, 13 Feb 2027Online liveWeekend (Sat)
10:00 AM – 5:00 PM IST		6 SaturdaysEnquire

Corporate batches for your team can be scheduled on dates that suit you.

Career outlook

Soaring demand, and where it takes you

Data engineering remains central to analytics and AI programmes, and organisations continue to build in-house data teams.

Qualitative outlook based on our industry experience; no statistics are claimed.

Career roles

Become an expert

Roles this programme prepares you for:

Become a Data Engineer

Builds and maintains pipelines and data platforms.

What you will do
  • Write SQL for transformation and analysis
  • Use Python to ingest and clean data
  • Design dimensional data models
Skills you will use
SQLPythonETL/ELTData modelling
Overview

About the course

An end-to-end foundation in data engineering: SQL and Python for data, data modelling, ETL and ELT pipelines, orchestration, data quality and the modern data stack. Participants build a working pipeline from raw files to modelled tables.

Pipelines, warehouses, lakes and the modern data stack explained hands-on, from first principles to a working end-to-end pipeline.

Audience

Why should I attend?

Analysts and BI developers moving into engineering

Software engineers new to data

Fresh graduates in induction programmes

Testers and support engineers working with data

Prerequisites

Basic SQL and some programming exposure (any language)

Level

Beginner to Intermediate

Format

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 per participant + GST
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.

Outcomes

What will you learn

  • Write SQL for transformation and analysis
  • Use Python to ingest and clean data
  • Design dimensional data models
  • Build ETL and ELT pipelines
  • Schedule and monitor pipelines
  • Add data-quality tests
Skills

Skills you will gain

Core

SQLPythonGit

Modelling

star schemasslowly changing dimensions

Pipelines

ingestiontransformationorchestrationtesting
Tools

Tools and platforms covered

SQL
Python
PostgreSQL
dbt
Apache Airflow
Git
Docker

Product names and logos are trademarks of their respective owners. Their use here does not imply endorsement.

Curriculum

Course curriculum

9 modules · 42 topics · hands-on labs · capstone project · 40 hours · 5 days

M01 The data landscape · 5 topics · 5 hrs
  • Warehouses, lakes and lakehouses
  • Batch vs streaming
  • Roles in a data team
  • Modern data stack overview
  • Data team operating models

🧪 Hands-on: Explore a sample platform

M02 SQL for engineering · 5 topics · 5 hrs
  • Joins and aggregations
  • Window functions
  • Incremental loads
  • CTEs
  • Query performance basics

🧪 Hands-on: Transform raw tables

M03 Python for data · 5 topics · 5 hrs
  • Files and APIs
  • pandas basics
  • Packaging scripts
  • Working with JSON
  • Virtual environments

🧪 Hands-on: Ingest an API into a database

M04 Data modelling · 5 topics · 5 hrs
  • Facts and dimensions
  • SCD types
  • Naming conventions
  • Star vs snowflake
  • Data vault overview

🧪 Hands-on: Model a sales domain

M05 Pipelines and orchestration · 5 topics · 5 hrs
  • ELT with dbt
  • Scheduling with Airflow
  • Idempotency
  • dbt tests and docs
  • Backfills

🧪 Hands-on: Orchestrate the pipeline

M06 Quality and operations · 5 topics · 5 hrs
  • Data tests
  • Monitoring
  • Documentation
  • Freshness monitoring
  • Incident basics

🧪 Hands-on: Add tests and docs

M07 Cloud storage and warehouses · 4 topics · 4 hrs
  • Object storage
  • Columnar formats (Parquet)
  • Warehouse loading patterns
  • Cost basics

🧪 Hands-on: Load Parquet files from object storage into a warehouse

M08 Introduction to Spark · 4 topics · 4 hrs
  • Why distributed processing
  • DataFrames
  • Reading and writing data
  • When not to use Spark

🧪 Hands-on: Transform a large dataset with PySpark

M09 Capstone: end-to-end pipeline · 4 topics · 2 hrs
  • Ingestion
  • Modelling
  • Orchestration
  • Documentation and demo

🧪 Hands-on: Build a tested, scheduled pipeline from API to dashboard-ready tables

Projects

Hands-on projects

End-to-end pipeline

Raw files to a tested, documented star schema, orchestrated daily.

Why us

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.

How we deliver →

Certificate

Certificate of completion

Participants who complete the labs and capstone receive a Learning Dots certificate of completion for Data Engineering Foundations. [confirm certificate wording / sample image]

Reviews

Learner reviews

“[Verified learner review for this course]”

[Name, role, company]

“[Verified learner review for this course]”

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Placeholder slots; to be replaced with genuine, permission-cleared reviews.

Trainer

Meet your trainer

Karthick Selvam

Founder · Data Engineering Corporate Trainer, Bengaluru

[Trainer bio, experience and areas of expertise]

Specialist trainers from our network may co-deliver advanced modules. [list co-trainers if any]

FAQ

Frequently asked questions

What prerequisites are needed?

Basic SQL familiarity is helpful; we cover the rest.

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.

Data Engineering Foundations

A closer look

About this course

An end-to-end foundation in data engineering: SQL and Python for data, data modelling, ETL and ELT pipelines, orchestration, data quality and the modern data stack. Participants build a working pipeline from raw files to modelled tables.

What is Data Engineering Foundations?

Pipelines, warehouses, lakes and the modern data stack explained hands-on, from first principles to a working end-to-end pipeline.

Who this course is for

Analysts and BI developers moving into engineering, Software engineers new to data, Fresh graduates in induction programmes, Testers and support engineers working with data.

What you will be able to do

Write SQL for transformation and analysis. Use Python to ingest and clean data. Design dimensional data models. Build ETL and ELT pipelines. Schedule and monitor pipelines. Add data-quality tests.

Career paths

Roles this course supports include Data Engineer, Analytics Engineer, ETL Developer. Data engineering remains central to analytics and AI programmes, and organisations continue to build in-house data teams.

Enquire about Data Engineering Foundations

Download course curriculum

We will email the detailed curriculum. [attach PDF curriculum]

Related reading

Data Engineering guides

What Is Data Engineering? A Plain-English Guide

Data engineering is the discipline of collecting, moving, transforming and storing data so analysts, applications and AI can use it reliably.

ETL vs ELT: What Is the Difference?

Two patterns for moving and transforming data, differing in where the transformation happens.

Data Lake vs Data Warehouse vs Lakehouse

Three architectures for storing analytical data.

What Is Apache Spark and When Should You Use It?

Apache Spark is an open-source engine for processing large datasets in parallel across a cluster.

All Data Engineering guides →