DBT Full Introduction: Data Build Tool + ELT Why When What (48 Min) #dataengineering

Description:
This is the complete, merged DBT introduction class from Time Variant AI School — combining both sessions on DBT into one continuous 48-minute, 23-second video (48:23): first, a full introduction to DBT (Data Build Tool) itself, and second, why the industry moved to ELT and exactly when and how DBT fits into that pattern.

Taught by Madhu Carpenter (24+ years of industry experience), this class gives you a complete first understanding of DBT — what it is, why it exists, and why it's built specifically for the "Transform" step in ELT — all explained in the context of the Bank Customer Analytics 360 project used throughout this course.

WHAT THIS CLASS COVERS
PART 1 — INTRO OF DBT (DATA BUILD TOOL)
- What DBT is, in plain terms: a SQL-first transformation tool that turns raw warehouse data into clean, tested, documented models
- The core building blocks of DBT: models (SQL SELECT statements), tests (data quality checks), documentation, and sources
- Why DBT is SQL-first, and why that makes it easy for SQL developers and analysts to become productive quickly
- How DBT brings software engineering discipline — version control, testing, modularity, documentation — into SQL-based transformation
- Where DBT sits in this course's pipeline: Oracle → Airflow → AWS S3 → Snowflake → DBT → Data Vault 2.0 → Power BI

PART 2 — DBT (ELT): WHY, WHEN, WHAT
- The difference between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform)
- WHY the industry shifted to ELT: cheap, elastic cloud compute (like Snowflake's Virtual Warehouses) made transforming data inside the warehouse faster and more efficient than transforming it beforehand
- WHEN to use ELT, and when a more traditional ETL-style step (like pre-load data masking) still makes sense, especially in regulated industries like banking
- WHAT ELT looks like in this course's architecture: Airflow handles Extract and Load, DBT handles Transform — and why DBT is built specifically as an ELT tool, not an ETL tool

DURATION
Total length: 48:23 — forty-eight minutes and twenty-three seconds. This merged class takes you from "what is DBT?" all the way to "why is DBT designed this way?" in one continuous, focused session.

CLASS NOTES — FULL SUMMARY
1. DBT (Data Build Tool): a transformation-only tool — it does not extract or load data; it assumes raw data is already sitting in the warehouse (Snowflake, in this course) and turns it into clean, modeled, tested tables.
2. Models: SQL SELECT statements saved as .sql files; DBT manages turning them into tables/views and running them in the correct dependency order.
3. Tests: data quality checks defined alongside models — e.g., checking a customer ID column is never null — a major reason DBT is trusted in production.
4. Documentation: DBT auto-generates a browsable site describing every model and how they depend on each other.
5. ETL vs ELT: ETL transforms data BEFORE loading it into the warehouse; ELT loads raw data first and transforms it AFTER, inside the warehouse — the modern, cloud-native default.
6. WHY the shift to ELT happened: elastic, affordable cloud compute (Snowflake Virtual Warehouses) made it cheaper and faster to transform data where it already lives, rather than in a separate processing system beforehand.
7. WHEN ELT makes sense: when your warehouse has strong compute, when you want raw data preserved for history/audit (a direct fit with Data Vault 2.0), and when transformation logic changes frequently.
8. WHEN pre-load transformation may still be needed: masking or removing highly sensitive data before it's allowed to land in the warehouse — an important compliance consideration in banking.
9. Pipeline mapping: Airflow = Extract + Load (Oracle → AWS S3 → Snowflake); DBT = Transform (raw Snowflake data → Data Vault 2.0 modeled tables) — this is exactly why Airflow and DBT are paired together in this course's architecture.

WHO THIS CLASS IS FOR
- Anyone who wants a complete, one-sitting understanding of DBT — both what it is and why it's designed the way it is
- SQL developers and analysts moving into Data Engineering or Analytics Engineering roles
- Data Engineers who've used Snowflake but not yet a dedicated transformation tool like DBT


JOIN THE FULL LIVE BATCH
This video covers DBT and ELT fundamentals completely. The full live batch takes you hands-on through building real DBT models, tests, and documentation directly on the Bank Customer Analytics 360 project's Snowflake tables, with doubt-clearing support, mock interviews, and a job guarantee (full fee refund if not placed). A free demo class is available before you enroll.

To join the free demo class or ask about the next batch schedule and fees, message directly on WhatsApp: +91 87124 75689