Data Build Tools

Efficient Data Ingestion and Transformation with dbt

Data is the foundation of modern decision-making, and organizations need efficient ways to ingest, clean, and transform it. dbt (Data Build Tool) has emerged as a game-changer in analytics engineering, enabling teams to ingest raw data and transform it into usable insights in an automated, scalable way.

In this guide, we will explore why dbt is crucial, the prerequisites for using it, the key concepts, and a step-by-step workflow for handling data ingestion and transformation with dbt.

1. Why is dbt Important for Data Ingestion and Transformation?

Organizations deal with vast amounts of raw data from multiple sources like databases, APIs, and external systems. However, raw data is often messy, inconsistent, and unsuitable for direct analysis.

Key Benefits of Using dbt

✔ Scalability – dbt handles large datasets efficiently, making it ideal for growing businesses.
✔ Modularity – dbt promotes modular, reusable SQL queries, reducing redundancy.
✔ Data Quality & Governance – Built-in testing and documentation ensure trustworthy data.
✔ Automation & Scheduling – dbt allows scheduled transformations, eliminating manual processing.
✔ Version Control – Git integration ensures collaboration and tracking of data changes.

💡 Example Use Case: A retail company ingests sales data from multiple regions. dbt helps them clean, standardize, and aggregate the data into a centralized, analytics-ready format for decision-making.

2. Prerequisites for Using dbt

Before implementing dbt, ensure the following prerequisites:

2.1 Basic Knowledge Requirements

  • SQL proficiency
  • Understanding of ETL/ELT workflows
  • Familiarity with data warehouses (Snowflake, BigQuery, Redshift, etc.)
  • Experience with Git and version control

2.2 Technical Setup

  • Python Installed: dbt runs as a Python package. Install it using: 
    pip install dbt
  • Database Connection: Configure dbt to connect with a data warehouse (e.g., Snowflake, BigQuery, Redshift).
  • Git Repository: dbt projects are managed in Git for collaboration.

3. What Will This Guide Cover?

This guide will walk you through a step-by-step dbt workflow for data ingestion and transformation:

✔ Understanding the dbt Workflow
✔ Setting Up dbt for Data Ingestion
✔ Transforming Raw Data into Analytical Models
✔ Automating and Testing Data Transformations
✔ Deploying dbt Pipelines Efficiently

By the end of this guide, you’ll have a fully functional dbt workflow for ingesting and transforming data.

4. Must-Know Concepts Before Implementing dbt Workflow

4.1 ETL vs. ELT: The Shift in Data Processing

✔ ETL (Extract, Transform, Load) – Data is transformed before loading into a warehouse.
✔ ELT (Extract, Load, Transform) – Raw data is loaded first, then transformed within the warehouse (preferred for dbt).

💡 Why ELT is Better?

  • Performance – Modern cloud data warehouses (Snowflake, BigQuery) handle transformations efficiently.
  • Scalability – Raw data is stored first, enabling flexible transformations later.

4.2 dbt Components

✔ Models – SQL transformations stored as .sql files.
✔ Sources – References to raw data in external systems.
✔ Seeds – Static CSV files loaded as tables.
✔ Snapshots – Track historical changes in data.
✔ Tests – Validate data integrity (e.g., unique IDs, non-null values).

4.3 dbt Workflow Overview

1️⃣ Ingest Raw Data → 2️⃣ Define Sources → 3️⃣ Transform Data with Models → 4️⃣ Validate & Test Data → 5️⃣ Deploy & Automate

5. Step-by-Step dbt Workflow for Ingestion and Transformation

Step 1: Ingesting Raw Data into the Warehouse

Before dbt can transform data, it must be ingested into the warehouse.

💡 Common Ingestion Methods:
✔ Batch Processing – ETL tools (Fivetran, Airbyte) load data periodically.
✔ Streaming – Real-time ingestion (Kafka, AWS Kinesis).

📌 Example: Raw Sales Data Table (sales_raw)

order_idcustomer_idamountorder_date
10100150.002024-01-10
10200275.502024-01-12

Step 2: Defining Sources in dbt (sources.yml)

We define the source tables in dbt to reference raw data correctly.

Create sources.yml:

version: 2

sources:
  - name: ecommerce
    schema: raw_data
    tables:
      - name: sales_raw
        description: "Raw sales transactions before processing"

📌 Why?

  • Tracks data lineage (where raw data comes from).
  • Provides clear documentation.

Step 3: Creating Transformation Models (staging_sales.sql)

Now, we clean and standardize raw sales data using dbt models.

Create staging_sales.sql:

WITH sales AS (
    SELECT 
        order_id, 
        customer_id, 
        amount AS total_amount, 
        CAST(order_date AS DATE) AS order_date
    FROM {{ source('ecommerce', 'sales_raw') }}
)

SELECT * FROM sales

📌 Why?
✔ Cleans column names (standardizes naming conventions).
✔ Converts data types (ensures consistency).

Step 4: Aggregating Data (monthly_sales.sql)

Now, let’s build an analytical model to calculate monthly revenue.

Create monthly_sales.sql:

WITH sales AS (
    SELECT * FROM {{ ref('staging_sales') }}
)

SELECT 
    DATE_TRUNC('month', order_date) AS month,
    SUM(total_amount) AS total_revenue
FROM sales
GROUP BY month
ORDER BY month

📌 Why?
✔ Uses ref to pull transformed sales data.
✔ Creates aggregated insights (monthly revenue).

Step 5: Implementing Data Testing (test_monthly_sales.yml)

To ensure data integrity, we write dbt tests.

Create test_monthly_sales.yml:

version: 2

models:
  - name: monthly_sales
    description: "Aggregated revenue per month"
    tests:
      - unique:
          column_name: month
      - not_null:
          column_name: total_revenue

📌 Why?
✔ Ensures unique months in the report.
✔ Prevents null values in revenue.

Run tests using:

dbt test

Step 6: Deploying & Automating dbt Pipelines

Once the transformations are built and tested, we deploy dbt workflows.

✔ Run Models:

dbt run

✔ Schedule Runs (Using dbt Cloud or Airflow)

airflow trigger_dag dbt_pipeline

📌 Why?
✔ Ensures automated data updates.
✔ Reduces manual effort.

6. Where and How to Use dbt Workflow?

Where to Use dbt?

✔ Analytics Teams – Clean and transform data for reporting.
✔ Data Engineering Teams – Standardize and structure ingested data.
✔ Business Intelligence (BI) Tools – Feed transformed data to Tableau, Power BI.

How to Use dbt?

1️⃣ Define Data Sources (sources.yml)
2️⃣ Create Models for Transformation (.sql files)
3️⃣ Implement Data Testing (.yml test files)
4️⃣ Schedule Runs & Automate Pipelines

Using dbt for data ingestion and transformation ensures clean, structured, and analysis-ready data. By following this workflow, teams can improve efficiency, automate transformations, and enhance data quality.

🚀 Start using dbt today and transform your raw data into meaningful insights!