Prabhu Joseph
Apache Hadoop Committer at The Apache Software Foundation
Kanyakumari, Tamil Nadu, India
Summary
🤩
Rockstar🎓
Top SchoolPrabhu Joseph is an Apache Hadoop Committer and seasoned distributed systems engineer with 11 years of experience building and hardening big data platforms across cloud and on-prem environments. He has led and shipped core YARN, Hive/Tez, and Flink work at organizations including AWS, Microsoft, Cloudera/Hortonworks and Visa, often bridging open-source projects with enterprise deployments. At AWS he led EMR teams for Flink and Hive on EC2/EKS/Serverless, and his contributions to apache/hadoop include logging migration to SLF4J, capacity scheduler fixes, and robustness improvements that reduced subtle workflow bugs. Known for pragmatic refactors and stronger unit tests, he pairs deep protocol-level knowledge of Hadoop YARN with cloud-native experience in Kubernetes, Docker and Go. Based in Kanyakumari, India, he combines active open-source stewardship with hands-on product delivery across large customer onboardings. His track record shows a knack for translating complex scheduler and resource-management challenges into reliable production systems.
11 years of coding experience
14 years of employment as a software developer
Bachelor's Degree Information Technology, Bachelor's Degree Information Technology at Sri Venkateswara College of Engineering
Stackoverflow
111reputation
9kreached
1answer
2questions
Github Skills (15)
yarn-berry10
yarn210
javas10
java10
yarn310
slf4j10
yarnpkg10
apache-hadoop10
scheduler9
testing9
cap9
sched9
gcc6
jna6
java-native-interface6
Programming languages (3)
Github contributions (5)
apache/hadoop
Mar 2019 - Jun 2021
Apache Hadoop
Role in this project:
Back-end Developer
Back-end DeveloperContributions:38 reviews, 24 commits, 28 PRs in 2 years 3 months
Contributions summary:Prabhu primarily focused on migrating logging APIs to slf4j within the hadoop-yarn-server-resourcemanager module, enhancing logging consistency. They also addressed issues related to resource management and capacity scheduling within the Hadoop YARN framework. Their contributions included fixing assertion statements and addressing multiple bugs by incorporating correct events in the workflow, showing a focus on improving the robustness of the system. Further contributions include code refactoring and improvements of unit tests.
apachebig-datasparkhadoopjava
PrabhuJoseph/High_Level_Design
Mar 2022 - Jun 2022
Contributions:88 pushes, 1 branch in 2 months
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