Sameer Agarwal
Co-Founder And CTO at The Apache Software Foundation
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolSameer Agarwal is a founder and CTO with 15 years building high-performance distributed query engines and database systems, currently leading product and engineering at Deductive AI in Mountain View. He was a founding engineer at Databricks and later led large-scale analytics teams at Facebook, where he drove query engine performance across geo-distributed clusters and earned top discretionary equity recognition. An Apache Spark committer and creator of BlinkDB from his UC Berkeley PhD, Sameer has deep expertise in query optimization, approximate queries, and production-grade deployment and billing systems. His open-source contributions to Spark and Presto include optimization work that reduced shuffles, enabled high-precision reads, and implemented TABLESAMPLE semantics—improvements now used widely in big-data pipelines. Colleagues rely on him to translate cutting-edge research into scalable systems that ship at internet scale, and he specifically enjoys applying statistical approximations to speed up real-world analytics.
15 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of California, BerkeleyBachelor of Technology (B.Tech.) Computer Science and Engineering, Bachelor of Technology (B.Tech.) Computer Science and Engineering at Indian Institute of Technology, Guwahati
Github Skills (24)
algorithm10
algorithms10
spark10
data-engineering10
big-data10
query-optimization10
data-structure10
java10
scala10
javas10
sql10
parquet10
data-structures10
query-engine10
presto10
Programming languages (4)
Github contributions (5)
apache/spark
Apr 2017 - Apr 2017
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 commit, 96 PRs, 587 comments in 1 day
Contributions summary:Sameer primarily focused on improving Apache Spark's SQL capabilities. They implemented optimizations such as preventing unnecessary shuffles during `take()` operations and adding support for skipping Hive metadata. The user also contributed significantly to constraint propagation, enabling the filtering of null values in filters and joins and reordering predicates for better performance. Furthermore, they introduced support for high-precision decimals and timestamps in the vectorized parquet reader.
analyticspythondata-processingsqlapache
prestodb/presto
Sep 2013 - Oct 2013
The official home of the Presto distributed SQL query engine for big data
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 1 comment in 1 month
Contributions summary:Sameer implemented and refined features within the Presto SQL query engine, focusing on the `TABLESAMPLE` operator and its associated functionalities. Key contributions include implementing the `TABLESAMPLE BERNOULLI` sampling method, addressing boundary condition edge cases, and integrating the `TABLESAMPLE` operator with the query planner. The user also developed approximate average aggregation operators, enhancing the system's analytical capabilities. The work demonstrates a strong understanding of query optimization and SQL engine internals.
distributed-sqlquerybigdataquery-enginesql
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