google/dwh-migration-tools
Jan 2026 – Feb 2026
2 Months active
Languages Used
JavaGroovy
Technical Skills
API IntegrationAPI integrationHTTP client managementJavaSpring FrameworkUnit Testing
Dominik Burdzy
PROFILE
Dominik Burdzy
During a two-month period, Dawid Burdzy enhanced the google/dwh-migration-tools repository by building features that improved observability, reliability, and compliance for Cloudera migrations. He implemented dynamic resource usage collection and expanded cluster telemetry, enabling detailed RAM and CPU metrics across hosts. Using Java, Groovy, and Apache Spark, Dawid centralized Spark YARN application data, automated Spark History Server discovery, and refined application type detection for better diagnostics. He also strengthened authentication handling and ensured license compliance, maintaining backward compatibility during output file transitions. The work demonstrated depth in backend development, data processing, and compliance management, resulting in more robust migration tooling.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
12Total
Bugs
2
Commits
12
Features
4
Lines of code
9,275
Activity Months2
Your Network
14 peopleWork History
February 2026
7 Commits • 2 Features
Feb 1, 2026February 2026 — google/dwh-migration-tools: Notable progress across metrics, Spark metadata, and compliance. Delivered expanded resource telemetry for Cloudera clusters, enabling total_physical_memory_used_across_hosts, total_physical_memory_total_across_hosts, cpu_user_rate, and cpu_system_rate across hosts to improve observability and capacity planning. Implemented Spark YARN metadata extraction and refined application type detection, including support for custom Spark history server names and improved detection by reading the Spark Java command from system properties. Completed license compliance updates ahead of release and maintained backward compatibility for resource allocation output files during the transition to new naming conventions. These changes increase reliability, traceability, and release readiness while enabling deeper insights into resource usage and Spark workloads.
7 Commits • 2 Features
Feb 1, 2026February 2026 — google/dwh-migration-tools: Notable progress across metrics, Spark metadata, and compliance. Delivered expanded resource telemetry for Cloudera clusters, enabling total_physical_memory_used_across_hosts, total_physical_memory_total_across_hosts, cpu_user_rate, and cpu_system_rate across hosts to improve observability and capacity planning. Implemented Spark YARN metadata extraction and refined application type detection, including support for custom Spark history server names and improved detection by reading the Spark Java command from system properties. Completed license compliance updates ahead of release and maintained backward compatibility for resource allocation output files during the transition to new naming conventions. These changes increase reliability, traceability, and release readiness while enabling deeper insights into resource usage and Spark workloads.
February 2026
January 2026
5 Commits • 2 Features
Jan 1, 2026January 2026: Delivered significant enhancements to the google/dwh-migration-tools project focusing on observability, reliability, and security. Implemented dynamic resource usage collection for Cloudera services, caching of Spark YARN applications, and automatic Spark History Server discovery via Knox, improving data retrieval latency and end-to-end visibility. Added cluster existence validation and authentication refactoring to support multiple methods, reducing failed tasks and tightening security. These changes establish a stronger foundation for scalable migrations and easier troubleshooting, with measurable business value in faster issue diagnosis, more reliable job execution, and improved security posture.
January 2026
5 Commits • 2 Features
Jan 1, 2026January 2026: Delivered significant enhancements to the google/dwh-migration-tools project focusing on observability, reliability, and security. Implemented dynamic resource usage collection for Cloudera services, caching of Spark YARN applications, and automatic Spark History Server discovery via Knox, improving data retrieval latency and end-to-end visibility. Added cluster existence validation and authentication refactoring to support multiple methods, reducing failed tasks and tightening security. These changes establish a stronger foundation for scalable migrations and easier troubleshooting, with measurable business value in faster issue diagnosis, more reliable job execution, and improved security posture.
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Quality Metrics
Correctness96.8%
Maintainability83.4%
Architecture88.4%
Performance85.0%
AI Usage31.8%
Skills & Technologies
Programming Languages
GroovyJava
Technical Skills
API IntegrationAPI integrationApache SparkHTTP client managementJavaREST APIsSoftware DevelopmentSparkSpring FrameworkUnit Testingbackend developmentcompliance managementdata processingopen source licensingunit testing
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