Model data the way real projects do.
A free live bootcamp for anyone who wants to become a data engineer. In 10 days you take four real business systems from raw tables to star schemas, physical DDL and ETL mapping sheets — hands-on in ERwin, reviewed by a working Data Architect.
You're registered.
Our team will WhatsApp you within 24 hours with the batch start date, timings and the ERwin setup guide for Day 1.
Pipelines break when the model is wrong.
Most beginners jump straight into PySpark, Databricks or Snowflake. Then they hit their first project and can't answer the basic question: what should this table look like?
Data modeling is that answer. It decides the grain, the keys, the history you keep and how fast the dashboard runs. Interviewers for data engineer roles test it every time, because every pipeline you write sits on top of a model somebody designed.
This bootcamp teaches the same flow used on enterprise projects — from understanding the business process to handing a mapping sheet to the ETL team.
Every cloud data warehouse sits on a data model.
Snowflake, BigQuery, Azure Synapse, Databricks, Fabric, Redshift — the logos change every few years. What doesn't change is the design underneath: facts, dimensions, grain, keys and history.
Learn modeling once and every platform becomes easier. You stop memorising features and start asking the right question: how should this data be shaped?
Data modeling is the stepping stone. The platform is just where you build it.
Snowflake
Star schemas in the gold layer, clustering keys on large facts, SCD2 with Streams and Tasks.
Google BigQuery
Fact tables PARTITION BY date and clustered on keys; when to denormalise with nested fields.
Azure Synapse
HASH distributed facts and REPLICATE dimensions — the choice comes straight from your model.
Databricks
Medallion layers in Delta Lake — bronze, silver, then a dimensional gold layer for BI.
Microsoft Fabric
Lakehouse and warehouse tables feeding a Power BI semantic model built as a star.
Amazon Redshift
DISTKEY and SORTKEY picked from the joins and filters your model defines.
Four systems. Four models in your portfolio.
You don't learn modeling from slides. Every concept is applied to a working business system, and every model you submit gets a review.
Order management
Reverse-read the source, write the data dictionary, check source data quality, then convert the ER model into a star schema.
Restaurant billing or school fee system
Generate DDLs, reverse-engineer them in ERwin, understand the business flow and design the dimensional model on your own.
A system you pick
Choose any transactional or operational system — hospital, logistics, fintech — and model it end to end as an independent assignment.
Live case study: supply-chain dashboard
Start from what the business wants to see on a dashboard and work backwards to the facts, dimensions and grain that power it.
One skill a day. Each builds on the last.
Every session is 90 minutes, live and hands-on. Open any day to see exactly what's covered and what you walk away with.
Day 01Data warehouse foundations and ERwin setup
- A real data architecture: sources, warehouse, BI
- What data modeling is: conceptual, logical, physical
- The data modeling life cycle
- ERwin installation and first hands-on model
Day 02Reading an ER model: order management
- Business entities, attributes, keys and relationships
- Business process and process flow
- Building a data dictionary with Report Builder
- Source data quality analysis and correction
Day 03From ER model to dimensional model
- Identify the grain, then the dimensions, then the facts
- Star schema vs snowflake schema
- Convert the order management model step by step
- Review of every student's model
Day 04Medallion architecture: bronze, silver, gold
- Source → bronze → silver → gold → semantic layer in Power BI
- Data model for all three layers, built in Excel
- Data load strategy document
- Quality checks, standardisation, harmonisation and cleansing — merging multiple customer source files
Day 05Reverse engineering: restaurant billing or school fees
- Generate realistic DDLs with ChatGPT
- Reverse-engineer the DDLs into an ERwin model
- Write entity and attribute definitions, map the business flow
- Transform to a dimensional model
Day 06Your own use case
- Pick any transactional or operational system
- Generate DDLs and reverse-engineer them
- Document process, entities, attributes and relationships
- Convert to a dimensional model and present it for review
Day 07Live case study: supply-chain dashboard
- Start from dashboard requirements, not tables
- Find the business processes behind each KPI
- Design the model individually or as a team
- Live review and discussion
Day 08Concepts deep dive and interview Q&A
- SCD Types 0, 1, 2, 3 — and when to use each
- Junk, conformed, degenerate and role-playing dimensions
- Additive, semi-additive and non-additive facts; transaction, periodic and accumulating fact tables
- Normalisation (1NF–3NF), sub-types, hierarchies, Kimball bus matrix
Day 09Physical modeling and model review
- Data types, indexes, partitions, storage and volumetrics
- Forward engineering, Check Model and DDL generation
- Reviewing a model from four sides: business, reporting, ETL and DBA
Day 10Data mapping for ETL and wrap-up
- Build a source-to-target mapping sheet
- Transformation rules the ETL team can code from
- Final review of your portfolio models
- Next steps into the full data engineering track
The full modeling vocabulary, applied — not memorised.
Every term below shows up in a model you build during the 10 days.
Modeling process
Business process, grain, dimensions, facts. Conceptual, logical and physical models. Forward and reverse engineering.
Dimensions
Type 0, 1, 2 (slowly changing) and 3. Junk, conformed, degenerate and role-playing dimensions.
Facts
Additive, semi-additive and non-additive facts. Transaction, periodic snapshot and accumulating fact tables.
Relationships
One-to-one, one-to-many, many-to-many and how to resolve it. Self-referencing, identifying and non-identifying.
Entities and normalisation
Strong, weak and associative entities. 1NF, 2NF, 3NF. Super-types with exclusive and inclusive sub-types.
Hierarchies
Parent-child, balanced, unbalanced and ragged. Static vs dynamic hierarchies.
Schemas and notation
Star and snowflake schemas. IE and IDEF1X notation. Kimball bus matrix and bus architecture.
Standards
Naming and abbreviation rules for tables and columns, descriptions, consistency, subject areas and model versioning.
Documentation
Data dictionaries, model comparison, mapping documents, and table, model and subject-area reports in PDF, XLS and CSV.
Built for people starting their data career.
If you want to become a data engineer, this is the right first step — before any cloud tool.
Students and freshers
Final-year and recent graduates who want a skill that shows up in every data engineering interview.
Career switchers
Testers, support engineers and SQL developers moving into data roles who need the design side of the job.
Pipeline builders
Engineers already writing ETL in Spark, ADF or dbt who have never designed the target model themselves.
BI and Power BI developers
Report builders who want to understand why a model is fast or slow, and design it right the first time.
Before you join: basic SQL (SELECT, JOIN) helps. No modeling experience needed. A laptop for ERwin — we guide the installation on Day 1.
Taught by a practitioner, not a theorist.
Sateesh Pabbathi
"I'm stuck in a low-paying job on PHP, .NET or Java. I want to get into data engineering — but I don't know where to start."
I hear this every week from IT professionals. I get it — I've been there. That's exactly why this bootcamp starts with data modeling: it's the foundation every data engineering role is built on.
I have 22+ years in cloud computing, data engineering and DevOps, from classic enterprise data warehouses to today's cloud platforms like Snowflake, Databricks and BigQuery, on global projects across AWS, Azure and GCP. Today I still work as a Data Architect on enterprise data platforms — so every model, review and interview tip here comes from live projects.
I mentor software developers, support engineers and testers working on legacy stacks, and guide them into well-paid cloud data engineering roles at product companies, MNCs and startups.
Before you register
Is it really free?
Yes. The full 10-day bootcamp costs nothing — no registration fee, no card details. We run it free so more students start their data career on the right foundation.
Do I need to know coding?
No programming is needed. Basic SQL helps when we read DDLs, but we explain everything as we go.
Which software do I need?
ERwin Data Modeler, Excel and a browser. We share the installation steps after you register and set everything up together on Day 1.
When are the sessions?
Ten live sessions of 90 minutes each. The batch start date and daily timings are shared on WhatsApp after registration.
What happens after the 10 days?
You'll have four reviewed data models and a mapping document for your portfolio. If you want to go further, we'll walk you through the 12-week Enterprise Ready Data Engineer program — real projects on AWS, Azure and GCP, mock interviews and career support. There's no pressure to enrol.
Start with the model. Build everything else on it.
Seats are kept small so every model gets a proper review. Register now and we'll send you the next batch date.
- Register with your WhatsApp number
- Get the batch date and ERwin setup guide within 24 hours
- Join Day 1 live and build your first model
You're registered.
Our team will WhatsApp you within 24 hours with the batch start date, timings and the ERwin setup guide for Day 1.