Ted Dunning
Fellow And CTO For Data Fabric
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTed Dunning is a veteran software researcher and technology leader with 16 years of experience, currently serving as Fellow and CTO for Data Fabric at Hewlett Packard Enterprise in Mountain View. He blends deep research expertise from HPE Labs with hands-on open source engineering as a long-time Apache PMC member and committer, notably contributing to Apache Drill's core query planner and the widely used t-digest algorithm for accurate online quantile estimation. Ted is known for tackling core data infrastructure problems—query parsing, physical plan interpretation, and probabilistic data structures—bringing academic rigor from a PhD (Sheffield) and MSc (New Mexico State) to production-grade systems. His profile reflects a rare combination of standards leadership at The Apache Software Foundation and practical algorithmic contributions that improve analytics at scale.
15 years of coding experience
PhD, PhD at Sheffield UniversityMSc, MSc at New Mexico State University
German
Stackoverflow
1,897reputation
162kreached
67answers
0questions
parallel-processing
top-5%
hadoop
top-5%
Github Skills (25)
algorithm10
algorithms10
apache-drill10
data-structure10
java10
javas10
query-parser10
antlr10
drilldown10
query-engine10
data-structures10
querying10
hadoop9
big-data9
sql9
Programming languages (18)
Github contributions (5)
tdunning/t-digest
Nov 2013 - Feb 2022
A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 reviews, 364 commits, 54 PRs in 8 years 4 months
Contributions summary:Ted primarily focused on the development of the `t-digest` data structure, contributing to the core source code. They added source code files and made changes to existing ones, which involved the implementation of the core functionality of this data structure, which accumulates rank-based statistics like quantiles. They also refactored and added tests for verifying the accuracy and functionality of the `t-digest` data structure.
statisticsquantileson-linequantileaccumulation
apache/drill
Sep 2012 - Jan 2018
Apache Drill is a distributed MPP query layer for self describing data
Role in this project:Back-end Developer
Back-end DeveloperContributions:34 commits, 4 PRs, 26 comments in 5 years 5 months
Contributions summary:Ted contributed to the Apache Drill project by implementing and improving the plan parser, which is essential for query execution. Their work involved developing a plan parser using ANTLR, and then improving error messages, debugging, and fixing tests. They also began working on the physical plan interpreter, which indicates a focus on the core query processing engine of Drill.
jdbcdatastreambig-datadatabasehadoop
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