Anant Aneja
Software Engineer at Ahana, an IBM company
New Delhi, Delhi, India
Summary
🤩
Rockstar🎓
Top SchoolAnant Aneja is a software engineer with 11 years of experience building scalable backend systems across Microsoft, AWS, and Ahana (an IBM company), currently based in New Delhi. He specializes in distributed query engines and performance optimization, contributing notable improvements to the popular Presto project—optimizing TPCH stats generation with multithreading and HyperLogLog and enhancing join visualization for easier debugging. At AWS and Microsoft he delivered production-grade, large-scale services, combining deep systems knowledge with pragmatic engineering. Colleagues describe him as iterative and focused—his personal objective, “Learn, apply, repeat,” summarizes a pragmatic approach to continuous improvement. Anant brings the discipline of a long-tenured engineer and a subtle preference for simplicity and efficiency, aiming to “be more like Ron Swanson” in spirit while shipping reliable, low-friction systems.
11 years of coding experience
15 years of employment as a software developer
Bachelor of Technology (B.Tech.) Computer Science, Bachelor of Technology (B.Tech.) Computer Science at National Institute of Technology Rourkela
English
Stackoverflow
21reputation
1kreached
0answers
1question
Github Skills (9)
javas10
big-data10
query-optimization10
presto10
java10
sql9
distributed-systems9
parquet9
caching6
Programming languages (9)
Github contributions (5)
prestodb/presto
Jul 2022 - Jan 2023
The official home of the Presto distributed SQL query engine for big data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:435 reviews, 5 commits, 50 PRs in 6 months
Contributions summary:Anant primarily focused on improving the Presto distributed SQL query engine's internal workings and performance. Their contributions include enhancing the Graphviz output for Join nodes, which improves debugging and visualization of query plans. The user also made significant improvements to the efficiency of TPCH statistics generation, including switching to multi-threaded processing and using HyperLogLog for more efficient memory usage. Additionally, they fixed several minor issues, like incorrect wall time reporting and improvements to parquet processing.
distributed-sqlquerybigdataquery-enginesql
aaneja/presto
Jul 2022 - Apr 2025
The official home of the Presto distributed SQL query engine for big data
Contributions:2 PRs, 243 pushes, 94 branches in 2 years 8 months
jdbchbasebigdatabig-datatrino
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