Russell Jurney
Agentic Knowledge Graph Consultant at The Apache Software Foundation
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolRussell Jurney is an agentic knowledge graph consultant and entrepreneur with 15+ years building large-scale graph systems, ML pipelines, and data-driven products from startups to enterprises. He designs and operationalizes entity-resolved knowledge graphs and agent workflows using LLMs, graph ML and big-data tooling—recently shrinking a 3 billion-node business graph to a 500M identity graph via semantic entity resolution. A prolific open-source committer, he’s contributed to Apache Druid, Pig and DataFu and authored O’Reilly books that codified “agile data science,” a methodology he coined in 2009. He blends hands-on backend and frontend engineering (e.g., pydruid and Druid site work) with product instincts honed at LinkedIn, Walmart and multiple founding CTO roles. Based in San Francisco, he trains teams to sustain graph+LLM solutions and prefers practical, repeatable systems that turn messy real-world data into actionable automation.
14 years of coding experience
18 years of employment as a software developer
Biology, Biology at Georgia State University
Russian, Spanish, Greek
Stackoverflow
4,917reputation
1.5mreached
57answers
41questions
mongodb
top-5%
amazon-s3
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python
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pandas
top-5%
Github Skills (45)
json10
python10
apache-pig10
css10
responsive-design10
kafka10
bash10
pyspark10
hadoop10
java10
mongodb-database10
javas10
html10
mongodb10
druid10
Programming languages (19)
Github contributions (5)
rjurney/Agile_Data_Code_2
Mar 2016 - Oct 2021
Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition
Role in this project:
Data Scientist & Backend Developer
Data Scientist & Backend DeveloperContributions:1 review, 1100 commits, 48 PRs in 5 years 8 months
Contributions summary:Russell primarily contributed to the project by implementing JSON Lines read/write functionality and creating utility functions to support data handling in Python. Their work included building scripts and libraries to manipulate data and interface with various systems, including installing and configuring anaconda. They set up and created scripts for the installation of Hadoop, Spark, and MongoDB, in addition to creating a data flow in PySpark and integrating with Elasticsearch.
pythonamazon-ec2datapredictive-analyticso-reilly
mongodb/mongo-hadoop
Dec 2011 - Feb 2015
MongoDB Connector for Hadoop
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 1 PR in 3 years 1 month
Contributions summary:Russell contributed to the MongoDB Connector for Hadoop by implementing features related to Pig integration. They added support for tuples and bags within the MongoStorage class, enabling data storage and retrieval. The user also addressed logging issues, removing excessive logging for each tuple stored. Furthermore, they made options in the MongoStorage class optional and adjusted the output of debug logging messages.
hadoopconnectormongodb
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