Jonathan Wei is a software engineer with 11 years of experience building backend systems and cloud networking software, currently based in Austin and working at Imply. He has deep domain expertise in high-performance data systems and real-time analytics—contributing code and documentation to the widely used Apache Druid project, including ingestion, SQL function support, and site documentation maintenance. His background spans network-focused engineering at Cisco and Packetcounter, where he implemented traffic engineering, sFlow processing, and dynamic dataplane controls for spine-leaf architectures. Jonathan’s early work includes OpenStack Neutron plugin development and a practical grounding in systems programming (C/C++) and Python for data-plane tooling. He combines production-grade backend development with a knack for clear technical documentation and operationally minded features that improve stability and data management.
Contributions:73 commits, 109 PRs, 63 pushes in 1 year 11 months
Contributions summary:Jonathan primarily focused on updating and correcting documentation for different versions of the Druid project website. Their commits involved extensive edits to existing documentation files, specifically redirecting older documentation pages to their newer, updated counterparts. These changes ensured that users are directed to the most current and relevant information, reflecting their role in maintaining accurate and up-to-date documentation for the Druid project.
Apache Druid: a high performance real-time analytics database.
Role in this project:
Backend Developer
Contributions:9 releases, 241 reviews, 490 commits in 7 years 3 months
Contributions summary:Jonathan's commits focus on backend development, primarily related to data ingestion and analysis within the Apache Druid project. Their contributions include adding support for parsing different data formats (BytesWritable strings) within the Hadoop Indexer and also enhancing the query capabilities through the addition of SQL function support. In addition, they worked on improving the performance, stability, and data management aspects of the system, while adding features for managing segment data within a Hadoop environment.
real-timebig-datadruiddatabasehadoop
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