Peter Ableda
Director Of Product Management at Cloudera
Budapest, Central Hungary, United States
Summary
👤
Senior🎓
Top SchoolPeter Ableda is a Director of Product Management who builds scalable, secure enterprise AI platforms that help organizations move from experimentation to confident production. With 12 years of experience at Cloudera spanning engineering, customer operations, and product leadership across Budapest, Austin, and Seattle, he excels at aligning product, engineering, and go-to-market teams to simplify complexity and deliver measurable customer outcomes. A hands-on technologist by training (MSc in Computer Engineering), he brings practical system-level insight—demonstrated by contributions to Apache Spark improving memory management and logging—to his product decisions. He favors open ecosystems and partnerships to drive responsible, production-ready AI, and is known for translating deep technical trade-offs into pragmatic roadmaps. Colleagues describe him as a strategist who still dives into code and low-level stability issues to ensure products are dependable in real-world deployments.
12 years of coding experience
13 years of employment as a software developer
Master of Science (MSc) Computer Engineering, Master of Science (MSc) Computer Engineering at Budapest University of Technology and Economics
Stackoverflow
25reputation
931reached
0answers
3questions
Github Skills (21)
spark10
big-data10
java10
scala10
javas10
yarn310
yarnpkg10
yarn-berry10
yarn210
debug9
debugging9
error-handling9
sql8
jdbc7
mongodb6
Programming languages (11)
Github contributions (5)
apache/spark
Feb 2015 - Dec 2016
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 PRs, 12 comments in 1 year 9 months
Contributions summary:Peter contributed to the Apache Spark project by addressing memory management issues and improving logging within the YARN environment. Their work included fixing memory byte formatting in error messages, adding minimum memory checks for drivers and executors in the StaticMemoryManager, and refining log messages for clarity. These changes suggest a focus on improving stability, debugging, and the overall user experience within the Spark ecosystem.
analyticspythondata-processingsqlapache
cloudera/community-ml-runtimes
Jan 2022 - Jul 2022
Contributions:17 reviews, 2 commits, 14 PRs in 6 months
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