
Product leader for query engines and AI search
I led product for the SQL Server query optimizer at Microsoft, where the features I shipped now run in billions of Azure SQL queries a day. I went on to lead the query engine and Atlas Search teams at MongoDB, Azure SQL Copilot and natural-language-to-SQL at Microsoft, and the Elasticsearch core platform at Elastic. I still build hands-on.
SQL Server query optimizer features I shipped at Microsoft, including Adaptive Joins, Memory Grant Feedback, and Parameter Sensitive Plan Optimization.
Led the MongoDB query engine and Atlas Search, and the Elasticsearch core platform, including vector and hybrid retrieval.
From the SQL Server optimizer to AI search.
A comprehensive course preparing SQL Server DBAs and Developers for AI-powered semantic search. Covers vector embeddings, DiskANN indexing, hybrid search, and production deployment.
View course details →Production patterns for real-world vector search, building on Essentials. Covers embedding selection, search quality metrics, reranking, RAG, and multi-modal search.
View course details →Earlier training: 13 SQL Server courses on Pluralsight →
Earlier books: four SQL Server titles from Apress →
Earlier paper: the SQL Server 2014 Cardinality Estimator whitepaper →
I write about agentic-first databases, where AI agents, not people, write the schemas and the queries.
What databases look like when AI agents become the primary users.
Smaller vectors, different results. How much drift can your use case tolerate?
How semantic reranking transforms vector search results.
Precision, recall, MRR, and nDCG: the metrics that tell you if vector search works.
System and method for cardinality estimation feedback loops in query processing. Co-invented at Microsoft.
View patent →