We see this anti-pattern everywhere:
Apps start small. The CEO asks for a report. The developer writes a heavy SQL query on the Production Database.
SELECT * FROM orders WHERE created_at > '2024-01-01'
It works fine for 1,000 rows.
When you hit 10 million rows, your app crashes because the analytics query locked the table.
At Programevi Engineering, we treat Analytics as a separate product with its own lifecycle.
The Shift: From OLTP to OLAP
If you are building a scalable system, you must separate your Transactional DB (OLTP) from your Analytical DB (OLAP).
Ingestion: Don't query live data. Use CDC (Change Data Capture) or nightly ETL jobs to move data.
Warehousing: Store it in a proper warehouse (BigQuery, Snowflake, or even a read-replica Postgres).
Visualization: Developers shouldn't write PDF reports. Connect a BI tool (PowerBI, Metabase) and let the business team explore the data.
Descriptive vs Predictive
Most engineering teams focus on "What happened?" (Logs, Metrics).
But the real value lies in "What will happen?".
By feeding this clean warehouse data into simple ML models, we help our clients predict stockouts or churn risks weeks in advance.
Architecture Deep Dive
If you want to stop being a "Report Generator" and start being a "Data Architect", check out our full guide on structuring a modern BI stack:
👉 Read the Full Guide: Data Analytics & BI Architecture
(Note: We discuss our approach to ETL pipelines and Data Warehousing in the article.)
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