Welcome to WordPress. This is your first post. EThe 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
3. Transformation with dbt
CREATE TABLE fact_sales ( sale_id NUMBER, date_key NUMBER, product_key NUMBER, customer_key NUMBER, amount DECIMAL(10,2), quantity INTEGER );
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
- Version control everything: Treat data pipelines as code
- Test data quality: Use dbt tests and Great Expectations
- Document models: Maintain clear documentation for stakeholders
- 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.dit or delete it, then start writing!