ETL Pipelines

We have implemented data projects for different clients across multiple continents.

ETL vs ELT

Context:

There was a need to consolidate data from multiple sources (PostgreSQL, external APIs, and Google Analytics) to feed performance dashboards and strategic reports.

Challenge:

Data was scattered across different silos, and manual reporting took an average of up to 4 days to consolidate.

Solution:

Implementation of ETL pipelines with Airflow and DBT. DBT allows SQL code versioning so that multiple Data Analysts can collaborate on the same Analytics project, and even roll back a specific report more easily. Structured data was stored in Databricks + Spark for large-scale processing. Visualization was handled with Power BI and Apache Superset, providing real-time insights.

Results:

Report consolidation time reduced from 2 days to 15 minutes

Automated dashboards serving multiple departments (Sales, Finance, and Product)

Data as a product, made available via APIs for integration with SaaS applications

Conclusion:

The company gained autonomy and started making real-time decisions, improving its competitiveness in the market.