Database Performance Tuning
Making the slow parts fast
Finding the queries and the missing indexes behind a system that has got heavier every year, and fixing the ones that matter.
Photo: wal_172619 / Pixabay 
Photo: congerdesign / Pixabay
Selected work demonstrating this service.
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A month of meter readings in 180 milliseconds
Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.
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Made a geographical data pipeline 50x faster
Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.
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Improved GIS map performance 10x on PostgreSQL
Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, including the data cutover, optimizing processing and data security.
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Ran 100 PostgreSQL, MS SQL, BigQuery warehouses
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.
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Accelerated PostgreSQL performance by 10x
Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.
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Quicker answers from a better choice of key
Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.
If this is the work you need done, the next step is a conversation.
Tell us the problem and we will tell you honestly whether we are the right people.
