Building Scalable Data Pipelines with BigQuery

Data is the new oil, but only if you can refine it. In this post, we explore how to build scalable data pipelines using Google Cloud Platform.

Why BigQuery?

BigQuery offers serverless, highly scalable, and cost-effective data warehousing. It integrates seamlessly with other GCP services.

The ETL Process

We recommend a modern ELT (Extract, Load, Transform) approach. Load raw data first, then transform it within the warehouse using dbt.

      // Example dbt model
      SELECT
        user_id,
        COUNT(*) as total_orders
      FROM
        raw_orders
      GROUP BY
        user_id
      

This approach allows for greater agility and easier debugging of data transformations.