Modern Data Stack: From Raw Data to Insights

The Modern Data Stack

The modern data stack has revolutionized how organizations handle data. Let's explore the key components and how they integrate.

Architecture Overview

graph TD A[Data Sources] --> B[Ingestion: Fivetran/Airbyte] B --> C[Storage: Snowflake/BigQuery] C --> D[Transformation: dbt] D --> E[BI: Tableau/Looker] D --> F[Reverse ETL: Hightouch] F --> G[Operational Systems]

1. Data Ingestion

Modern ingestion tools like Fivetran and Airbyte provide:

• Pre-built connectors for popular sources
• Automatic schema detection and evolution
• Change data capture (CDC) capabilities

2. Cloud Data Warehouse

Snowflake, BigQuery, or Redshift serve as the central repository:

-- Example: Creating a fact table CREATE TABLE fact_sales (    sale_id NUMBER,    date_key NUMBER,    product_key NUMBER,    customer_key NUMBER,    amount DECIMAL(10,2),    quantity INTEGER );

3. Transformation with dbt

dbt (data build tool) handles transformation in SQL:

-- models/marts/fct_sales.sql {{ config(materialized='table') }} SELECT    s.sale_id,    s.sale_date,    s.amount,    c.customer_name,    p.product_name FROM {{ ref('stg_sales') }} s LEFT JOIN {{ ref('dim_customers') }} c ON s.customer_id = c.customer_id LEFT JOIN {{ ref('dim_products') }} p ON s.product_id = p.product_id

Best Practices

1. Version control everything: Treat data pipelines as code
2. Test data quality: Use dbt tests and Great Expectations
3. Document models: Maintain clear documentation for stakeholders
4. Monitor pipeline health: Set up alerts for failures

Real-World Example

Here's a complete example of a modern data pipeline:

# dbt_project.yml name: 'company_analytics' version: '1.0.0' models:  company_analytics:    staging:      materialized: view      schema: staging    marts:      materialized: table      schema: analytics

Conclusion

The modern data stack provides a flexible, scalable approach to data analytics. Choose components that fit your specific needs and scale with your organization.