# Database Performance Tuning

> Selected work demonstrating this service.

- [Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.](https://engineer.company/portfolio/engineered-hourly-electricity-consumption-aggregation-pipeline-in-python-4/)
- [Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.](https://engineer.company/portfolio/accelerated-geographical-data-pipeline-performance-by-50x-by-5/)
- [Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, optimizing processing and data security.](https://engineer.company/portfolio/improved-geographical-map-application-performance-by-10x-through-6/)
- [Architected, created, and managed 100 PostgreSQL, MS SQL, and Google BigQuery data warehouse databases with primarily GIS and time‑series data, optimizing performance and scalability.](https://engineer.company/portfolio/architected-created-and-managed-100-postgresql-ms-sql-12/)
- [Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.](https://engineer.company/portfolio/accelerated-postgresql-performance-by-10x-via-strategic-indexing-15/)
- [Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.](https://engineer.company/portfolio/adopted-uuid-v7-time-ordered-identifiers-postgresql-18-64/)

<https://engineer.company/services/database-performance/>