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Tutorial Series Introduction & Overview

The 90-Day Data Quest

90 Days. One Quest. From Your First SQL Query to Building Autonomous AI Data Systems.

This is not another "learn SQL in a weekend" tutorial. It's a complete, project-driven quest that takes you from absolute beginner to a data professional who can architect AI-native systems in 90 days — covering SQL, Advanced SQL, BI, Data Engineering, Analytics, Data Warehousing, MPP, PySpark, Snowflake, and Agentic AI.

Mudit Kumar · 10 min read · Series Introduction · June 2026 · github.com/MuditKumarR/dataquest90

Let me be direct about what this is — and what it is not.

There are hundreds of "learn SQL" tutorials on the internet. Most teach you a SELECT statement in isolation, celebrate that, and leave you there. You finish, open a real job description, and suddenly you're reading about dbt, Snowflake, partitioning, ETL pipelines, data lakes, vector stores, and AI agents — none of which the tutorial mentioned.

The 90-Day Data Quest was built to close that gap completely. One structured path, 90 days, from SELECT * FROM table all the way to an AI agent that queries your database autonomously.

"The goal is not to teach you SQL. The goal is to make you the kind of engineer who can look at any data problem — from a simple report to an AI agent querying a lakehouse — and know exactly what to do."

What Is The 90-Day Data Quest?

The 90-Day Data Quest is a free, open-source, project-driven curriculum that teaches the entire modern data stack as one connected journey over 90 days — not ten disconnected courses. The idea is simple: treat learning data like a quest, with a clear destination and a path you actually finish. Every concept is taught in context. Every technology is explained through its relationship to everything else.

You won't just learn SQL here and data engineering somewhere else and Snowflake in a vendor tutorial. You'll see how a decision made at the SQL layer has consequences six layers up in an AI system. When you finish, you won't have a pile of disconnected skills. You'll have a mental model of how the whole thing fits together.

The core premise: In 2026, the most valuable data professionals are not specialists in one tool. They are engineers who move fluidly across SQL, pipelines, warehouses, and AI systems — understanding how each layer feeds the next.

What This Series Offers

Every module is hands-on. You don't watch — you build. Here's what's inside each one:

📦

Real Datasets

Every module ships with realistic datasets and ready-to-run setup scripts — no toy examples.

🏢

Business Scenarios

You solve problems for a fictional company, DataVerse Inc. — the way real work actually arrives.

💻

Runnable Code

Every query and script is in the GitHub repo. Clone it, run it, break it, learn from it.

🎯

Progressive Exercises

Each module ends with exercises that get harder — plus solutions so you can check your work.

🧩

Capstone Projects

Build complete platforms: HR analytics, sales intelligence, a warehouse, an AI data agent.

🔗

Everything Connected

Each module explicitly links back to the ones before — so the knowledge compounds.

Who This Series Is For

The 90-Day Data Quest meets you wherever you are. It works whether you've never written a query or you've been a data engineer for years and want to understand the AI layer being built on top of your work.

🌱

Complete Beginners

Never written SQL? Start at the very first module. No prerequisites.

🔄

Career Switchers

Moving into data from another field — get a complete, structured path.

📊

Data & BI Analysts

Know basic SQL but never built a pipeline or warehouse? This fills the gap.

⚙️

Software Engineers

Want to move into data engineering with a solid foundation, not guesswork.

🏗️

Data Engineers

Strengthen the fundamentals and understand the AI systems on top of your data.

🤖

AI Engineers

Understand how structured data actually works at scale — the layer your models depend on.

What You'll Achieve

This is the concrete outcome. By the time you finish the 90-Day Data Quest, you will be able to:

The career payoff: SQL is the only skill required at every level — junior analyst, senior data engineer, data architect, and AI system builder. The 90-Day Data Quest takes you across all of them in one path.

The Full Stack — One Connected Journey

Here's the mental model the whole series is built on. SQL is not one layer among many. It's the language every layer above it is ultimately written in.

THE COMPLETE 90-DAY DATA QUEST STACK AGENTIC AI LAYER AI Agents · Text-to-SQL · RAG Pipelines · Autonomous Query Systems WAREHOUSE & SNOWFLAKE LAYER Snowflake · Star Schema · SCDs · MPP · dbt DATA ENGINEERING LAYER ETL/ELT Pipelines · Airflow · PySpark · CDC · Data Quality BI & ANALYTICS LAYER Dashboards · KPIs · Cohort Analysis · Revenue Reports · Executive Metrics SQL FOUNDATION SELECT · WHERE · JOIN · GROUP BY · Window Functions · CTEs · Performance Tuning Everything above is built on this SQL is not one layer. It is what every layer is written in.

The Learning Modules

The journey is split into five phases built on top of each other. You can't build a warehouse without SQL. You can't build an AI agent without understanding the data it queries. The sequence is deliberate.

Phase 1
SQL Mastery
2 modules
Phase 2
BI & Analytics
2 modules
Phase 3
Data Engineering
2 modules
Phase 4
DWH + Snowflake
3 modules
Phase 5
Agentic AI
1 module

Each module below links to its own folder in the GitHub repository. SQL is available now — the rest are being published in sequence. Bookmark this page; the links go live as each module ships.

01 SQL
SQL Fundamentals
From nothing to everything: SELECT, WHERE, JOINs, GROUP BY, window functions, CTEs, and subqueries — taught through real business problems.
Open on GitHub
02 SQL
Advanced SQL & Optimization
Execution plans, indexes, partitioning, EXPLAIN ANALYZE — writing queries that stay fast at a billion rows.
Coming soon
03 BI
Business Intelligence
What BI really is, reporting vs analytics, and how SQL powers every dashboard you've ever seen.
Coming soon
04 Analytics
Analytics & Metrics
Revenue dashboards, cohort analysis, funnels, retention, and executive-level KPIs built with SQL.
Coming soon
05 Data Eng
Data Engineering
ETL vs ELT, batch vs streaming, pipelines, orchestration with Airflow — the full mental model.
Coming soon
06 PySpark
PySpark & Distributed Compute
When SQL isn't enough — distributed DataFrames, transformations, and processing at petabyte scale.
Coming soon
07 DWH
Data Warehousing
Star & snowflake schemas, fact and dimension tables, SCD types, and dimensional modeling.
Coming soon
08 MPP
MPP & Columnar Storage
How massively parallel processing works, why columnar databases are fast, and where traditional databases stop.
Coming soon
09 Snowflake
Snowflake
Architecture, virtual warehouses, micro-partitions, Streams, Tasks, Dynamic Tables, time travel, and cost.
Coming soon
10 Agentic AI
Agentic AI & Text-to-SQL
RAG over databases, Text-to-SQL, and building an AI agent that queries your data autonomously.
Coming soon
Why SQL is first — and matters more than ever: The rise of Text-to-SQL and AI agents made SQL more important, not less. An LLM generates plausible SQL — not correct SQL. You need to understand SQL well enough to spot when the AI is wrong, and it will be wrong on anything non-trivial. SQL is not a stepping stone to the AI layer. It's the foundation the AI layer is built on.

The GitHub Repository

Everything — every SQL script, dataset, exercise, and project — lives in one public repository. Clone it, run it, fork it, build on it.

dataquest90 — The Complete Tutorial Repository

All scripts, datasets, exercises, and projects are published at:

github.com/MuditKumarR/dataquest90

Each module corresponds to a folder in the repo. The README has the full index. Star the repo to follow along as new modules ship.

How to Start

What You Need

Your First Step

  1. Clone the repo: git clone https://github.com/MuditKumarR/dataquest90
  2. Open README.md for the full index and setup instructions
  3. Start with Module 01 — SQL Fundamentals (available now)
  4. Run the scripts, complete the exercises, check your answers in the solutions folder
Quick start: git clone https://github.com/MuditKumarR/dataquest90 — then open Module 01 and write your first query. Day 1 of 90 starts here.

If you have ever looked at a data engineering job description and felt like you were reading a foreign language, the 90-Day Data Quest will translate every word of it. Start the first module today — and star the GitHub repository so you know when the next one ships.

SQL Data Engineering Snowflake PySpark Agentic AI Tutorial Series Career Data Warehouse PostgreSQL BI MPP Analytics