ELT Pipeline using DBT and Snowflake

 Here’s a complete explanation with design + implementation steps for building ELT pipelines using DBT (Data Build Tool) and Snowflake, tailored for interview or project showcase.

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✅ Project Overview: ELT Pipeline using DBT and Snowflake

🎯 Goal:

To build an end-to-end ELT pipeline that:

• Extracts raw data into Snowflake

• Transforms it using DBT models

• Loads curated data into Snowflake data marts

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🧱 Architecture Diagram:

            ┌────────────┐
            │  Source DB │ (CSV, API, etc.)
            └────┬───────┘
                 │
         [Extract & Load]
                 │ (Airbyte/Fivetran/Custom Python)
                 ▼
         ┌───────────────┐
         │ Raw Snowflake │
         │   Schema      │  ← EL
         └────┬──────────┘
              │
         [Transform - DBT]
              ▼
   ┌─────────────────────┐
   │ DBT Staging Models  │  →  Clean + Cast
   ├─────────────────────┤
   │ DBT Intermediate    │  →  Joins + Enrich
   ├─────────────────────┤
   │ DBT Mart Models      │  →  BI-ready tables
   └─────────────────────┘
              │
              ▼
       ┌────────────┐
       │ BI Tools   │ (Power BI / Tableau)
       └────────────┘

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⚙️ Tech Stack:

• Snowflake – Cloud Data Warehouse

• DBT (Core or Cloud) – Data transformation tool

• Python/Airbyte/Fivetran – Data ingestion

• Power BI / Tableau – Optional for reporting

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🔁 ELT Pipeline Steps

🔹 1. Extract & Load (EL)

Use Python / Airbyte / Fivetran to load data into raw Snowflake schema:

# Example Python + Snowflake Connector
import snowflake.connector
import pandas as pd

df = pd.read_csv("orders.csv")

conn = snowflake.connector.connect(
    user='your_user',
    password='your_pass',
    account='your_account'
)

cursor = conn.cursor()
cursor.execute("USE DATABASE analytics; USE SCHEMA raw;")

for _, row in df.iterrows():
    cursor.execute(f"""
        INSERT INTO orders_raw (order_id, customer_id, amount)
        VALUES ('{row['order_id']}', '{row['customer_id']}', {row['amount']});
    """)

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🔹 2. Transform using DBT

🗂️ Project Structure:

dbt_project/
│
├── models/
│   ├── staging/
│   │   └── stg_orders.sql
│   ├── intermediate/
│   │   └── int_order_enriched.sql
│   └── marts/
│       └── mart_sales_summary.sql
├── seeds/ (optional)
├── dbt_project.yml
└── profiles.yml

🧱 Example DBT Models:

✅ staging/stg_orders.sql

{{ config(materialized='view') }}

SELECT
    order_id,
    customer_id,
    CAST(amount AS FLOAT) AS amount,
    CAST(order_date AS DATE) AS order_date
FROM {{ source('raw', 'orders_raw') }}

✅ intermediate/int_order_enriched.sql

{{ config(materialized='view') }}

SELECT
    o.*,
    c.customer_name,
    c.region
FROM {{ ref('stg_orders') }} o
JOIN {{ ref('stg_customers') }} c
  ON o.customer_id = c.customer_id

✅ marts/mart_sales_summary.sql

{{ config(materialized='table') }}

SELECT
    region,
    COUNT(order_id) AS total_orders,
    SUM(amount) AS total_sales
FROM {{ ref('int_order_enriched') }}
GROUP BY region

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⚙️ DBT Commands

# Initialize DBT project
dbt init dbt_project

# Run models
dbt run

# Test data quality
dbt test

# Generate documentation
dbt docs generate
dbt docs serve

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📊 Optional: Power BI or Tableau

Connect Snowflake curated tables (marts) to visualize metrics like:

• Sales by Region

• Monthly Orders

• Churned Customers

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✅ Key Benefits for Interview:

🔑 What to Highlight:

• Modular SQL with ref() for maintainability

• Version control and CI/CD-friendly (GitHub Actions)

• Data quality with dbt test

• Scalable architecture with raw/stage/mart layers

• Explain raw → curated flow

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📌 Resume Bullet Example:

🔹 Designed and implemented a modular ELT pipeline using DBT and Snowflake, transforming raw customer and order data into business-ready marts, improving query performance and reducing manual SQL effort by 60%.

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Would you like:

• A sample GitHub repo or code export?

• A PowerPoint version of this pipeline?

• Integration with Airflow or CI/CD?

Let me know how summarize for easy explanation for interview remember

ChatGPT said:

Here’s a simplified summary to easily explain ELT using DBT and Snowflake in an interview — short, clear, and structured:

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✅ ELT Pipeline using DBT and Snowflake – Interview Summary

🎯 Objective:

To move raw data into Snowflake and transform it using DBT into clean, analysis-ready tables.

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🧱 3-Layer Architecture (ELT)

1. Extract & Load (EL)

• Use tools like Python, Airbyte, or Fivetran

• Load raw data into Snowflake’s raw schema

“We first load raw data (CSV/API) into Snowflake tables using automated scripts or connectors.”

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2. Transform with DBT (T)

Use DBT to create three layers of models:

🔹 Staging Layer

• Cleans column names

• Casts data types

• Applies basic filters

“This ensures raw data is readable and consistent.”

🔹 Intermediate Layer

• Joins multiple tables

• Adds business logic or derived columns

“We enrich the data here for further analysis.”

🔹 Mart Layer

• Final summary tables for BI tools (e.g., sales by region)

“This layer is optimized for dashboards and reporting.”

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⚙️ DBT Key Features

• ref() manages dependencies

• dbt test for data quality checks

• dbt docs for auto-generated documentation

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💡 One-liner for Interview

“I built a scalable ELT pipeline where raw data is loaded into Snowflake, then transformed using DBT into clean, reusable models organized into staging, intermediate, and marts layers.”

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🧠 Tips to Remember

Layer	Purpose	Example
Raw	Just loaded data	CSV or API dump
Staging	Clean + format	Rename columns, cast data
Intermediate	Logic + joins	Add customer name to order
Marts	Final summary tables	Sales by region, churn