Overview:
Designed a comprehensive, developer-facing guide focused on SQL performance optimization for PostgreSQL and BigQuery environments. The guide served as a reference for improving query speed, reducing resource consumption, and applying database best practices across analytics and transactional workloads.
Key Contributions:
Structured Documentation: Developed a modular guide with clear sections on indexing strategies, query planning, join optimization, partitioning, and caching.
Knowledge Extraction: Collaborated with senior database engineers to translate undocumented tribal knowledge and performance hacks into structured, reusable documentation.
Explain Plan Analysis: Included annotated EXPLAIN and EXPLAIN ANALYZE outputs with detailed breakdowns of cost, rows, and runtime behavior.
Hands-On Examples: Provided copy-paste-ready SQL examples with sample datasets, expected outputs, and anti-patterns.
Impact: Enabled teams to reduce query execution time by 30–60% on high-volume reporting workloads; improved BigQuery cost management by identifying and fixing inefficient scans.
Tech & Tools:
PostgreSQL, BigQuery, SQL, Google Cloud Console, Markdown, Data Studio, GitHub, Jupyter Notebooks.
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