Sivabalan Narayanan
Software Engineer at Onehouse
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSivabalan Narayanan is a seasoned software engineer with 13 years of experience focused on distributed systems and datalakes, currently based in Mountain View and working at Onehouse. He has deep backend expertise in Java, Scala and C++, and hands-on experience with Spark, Hadoop/HDFS, Cronet and QUIC from roles at Uber, LinkedIn and Oracle. As an active Apache Hudi PMC contributor, he has improved Merge-on-Read semantics, partition-based deletes and index performance—demonstrating a commitment to production-grade data infrastructure and rigorous testing. His academic background in distributed systems from UC Santa Barbara complements practical work building scalable shared-memory and data-platform components. Notably, he blends research-informed design with pragmatic fixes for edge cases like null handling and index lookup bottlenecks, making him effective at both feature development and performance tuning.
13 years of coding experience
8 years of employment as a software developer
Master of Science (MS) Computer Science, Master of Science (MS) Computer Science at UC Santa BarbaraBachelor of Engineering (BE) Computer Science, Bachelor of Engineering (BE) Computer Science at PSG College of Technology
Github Skills (7)
javas10
spark10
apache-hudi10
java10
data-engineering9
database7
databases7
Programming languages (4)
Github contributions (5)
apache/hudi
Nov 2019 - Jan 2023
Upserts, Deletes And Incremental Processing on Big Data.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 4301 reviews, 428 commits in 3 years 3 months
Contributions summary:Sivabalan's commits primarily focus on improving the Apache Hudi codebase, specifically related to the implementation of the Merge on Read table type, including adding support for various features such as partition-based deletion and the integration of column stats index to optimize performance. The user also worked on fixing the performance problems related to the index lookup. Furthermore, they addressed issues with the handling of null values and improved the integration tests, confirming a dedication to code quality and addressing bugs within the project.
stream-processingbigdatahudiincrementalapacheflink
nsivabalan/hudi
Nov 2018 - Mar 2025
Spark Library for Hadoop Upserts And Incrementals
Contributions:1 review, 9 PRs, 1999 pushes in 6 years 5 months
bigdataemrspark-mlsparkapache-spark
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