DataDog/dd-trace-py
Oct 2025 – Mar 2026
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentDistributed TracingObservabilityPythonfull stack developmentinstrumentation
Yakov Shapiro
PROFILE
Yakov Shapiro
Yakov Shapiro enhanced observability and security for machine learning telemetry pipelines across the DataDog/dd-trace-py and DataDog/system-tests repositories. He developed instrumentation for Ray integration, enabling end-to-end tracing of object storage operations and introducing an AI Observability spans flag to support accurate billing for AI workloads. In parallel, Yakov implemented a secure authentication plugin for MLFlow, injecting Datadog API keys into HTTP requests based on environment variables, thereby strengthening telemetry data security in transit. His work, primarily in Python and JSON, emphasized backend development, distributed tracing, and configuration management, demonstrating depth in aligning cross-repository configuration and testing strategies for robust telemetry.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
2,418
Activity Months2
Your Network
1074 peopleWork History
March 2026
2 Commits • 2 Features
Mar 1, 2026March 2026 was focused on delivering two high-impact features across DataDog/system-tests and DataDog/dd-trace-py, strengthening telemetry reliability and security for ML telemetry pipelines. System-tests now align with upstream dd-go configuration updates, while dd-trace-py introduces a secure MLFlow telemetry authentication plugin. These efforts reduce configuration drift, boost observability, and enhance the security posture of telemetry data in transit.
2 Commits • 2 Features
Mar 1, 2026March 2026 was focused on delivering two high-impact features across DataDog/system-tests and DataDog/dd-trace-py, strengthening telemetry reliability and security for ML telemetry pipelines. System-tests now align with upstream dd-go configuration updates, while dd-trace-py introduces a secure MLFlow telemetry authentication plugin. These efforts reduce configuration drift, boost observability, and enhance the security posture of telemetry data in transit.
March 2026
October 2025
2 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for DataDog/dd-trace-py: Ray integration observability enhancements with AI Observability billing differentiation. Implemented instrumentation around ray.put calls to trace object storage operations and introduced an AI Observability spans flag to classify AI-related spans for correct billing. This work strengthens observability, billing correctness, and data-driven decisions for ML workloads.
October 2025
2 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for DataDog/dd-trace-py: Ray integration observability enhancements with AI Observability billing differentiation. Implemented instrumentation around ray.put calls to trace object storage operations and introduced an AI Observability spans flag to classify AI-related spans for correct billing. This work strengthens observability, billing correctness, and data-driven decisions for ML workloads.
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Quality Metrics
Correctness95.0%
Maintainability85.0%
Architecture95.0%
Performance85.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
API integrationBackend DevelopmentDistributed TracingObservabilityPythonapplication securitybackend developmentconfiguration managementfull stack developmentinstrumentationplugin developmenttelemetrytracingunit testing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
DataDog/system-tests
Mar 2026 – Mar 2026
1 Month active
Languages Used
JSON
Technical Skills
application securityconfiguration managementtelemetry