Indhumathi M is a Staff Data Engineer based in Bengaluru with 8 years of experience and 4.5 years focused deeply in the Big Data domain, specializing in file formats like Apache CarbonData and Parquet and metadata-driven optimizations for faster analytics. She has progressed through senior engineering and technical lead roles at Huawei before joining Visa, where she now works on large-scale data engineering problems. As an Apache CarbonData committer and PMC member she contributes upstream to the storage format she architects for production systems, and she also contributes fixes and schema work to flagship projects like Apache Hive. Her strengths include back-end data store development, schema and metadata management, and pragmatic performance tuning across Spark, Hive, Trino and Hadoop ecosystems. Colleagues rely on her for translating storage-level improvements into measurable query performance gains and robust upgradeability in complex data platforms.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering - BE Computer Science and Engineering, Bachelor of Engineering - BE Computer Science and Engineering at Sri krishna college of Engineering and Technology
Contributions:338 reviews, 183 commits, 170 PRs in 4 years 6 months
Contributions summary:Indhumathi primarily contributed to the development of the back-end functionality of a high-performance data store solution, as evidenced by their involvement in adding test cases for setting parameters dynamically. Their work focused on the implementation of features related to data loading and management, as seen in their modifications to existing code within the integration and core modules. The user demonstrated proficiency in Java and related CarbonData specific libraries, as the commits focus on testing specific configurations and behaviors within the storage system.
Contributions:17 reviews, 10 PRs, 22 comments in 1 year 7 months
Contributions summary:Indhumathi primarily contributes to the Apache Hive project, focusing on fixing schema inconsistencies, improving database interactions, and addressing issues related to partition filtering. Their work involves modifying SQL schemas, updating upgrade scripts, and fixing syntax errors in Hive-specific SQL files. They also upgrade dependencies and run tests, demonstrating a focus on database related maintenance and enhancements within the Hive ecosystem.
flinksqlapachebig-dataspark
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