a10pepo/EDEM_MDA2526
Oct 2025 – Jan 2026
4 Months active
Languages Used
DockerfileMarkdownPythonSQLShellYAMLbashplaintext
Technical Skills
Basic conditional logicDockerDocumentationFile handlingFile managementLinux command line
Jorgemart9
PROFILE
Jorgemart9
Jorge Martinez contributed to the a10pepo/EDEM_MDA2526 repository by developing end-to-end data engineering solutions over four months. He built streaming data pipelines using PySpark and Kafka to enable real-time sensor data ingestion, transformation, and anomaly detection, supporting live analytics for IoT use cases. Jorge also delivered a DBT project leveraging DuckDB and SQL for standardized sales and customer analytics workflows. His work included containerized Python utilities, Linux command line practice suites, and PostgreSQL schema design, demonstrating proficiency in Python, SQL, and Docker. The projects emphasized reproducibility, hands-on learning, and scalable analytics, reflecting a solid foundation in modern data engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
8
Lines of code
6,791
Activity Months4
Your Network
56 peopleWork History
January 2026
1 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — Delivered an end-to-end Streaming Data Processing Notebook (PySpark) for Kafka in repo a10pepo/EDEM_MDA2526. The notebook implements data ingestion from Kafka, transformation, windowed aggregations, and anomaly detection, enabling real-time streaming analytics. No major bugs reported this month. Overall impact: provides near real-time insights and alert-ready data pipelines, establishing a scalable foundation for streaming analytics and downstream dashboards. Technologies demonstrated include PySpark, Kafka, notebook-based data pipelines, streaming transformations, anomaly detection, and Git-based version control. This work strengthens data engineering capabilities and accelerates time-to-insight for streaming data use cases.
1 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — Delivered an end-to-end Streaming Data Processing Notebook (PySpark) for Kafka in repo a10pepo/EDEM_MDA2526. The notebook implements data ingestion from Kafka, transformation, windowed aggregations, and anomaly detection, enabling real-time streaming analytics. No major bugs reported this month. Overall impact: provides near real-time insights and alert-ready data pipelines, establishing a scalable foundation for streaming analytics and downstream dashboards. Technologies demonstrated include PySpark, Kafka, notebook-based data pipelines, streaming transformations, anomaly detection, and Git-based version control. This work strengthens data engineering capabilities and accelerates time-to-insight for streaming data use cases.
January 2026
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 Summary: Delivered a real-time streaming data processing capability for sensor data, establishing a scalable pipeline using PySpark and Kafka that enables ingestion, processing, and analytics in near real-time. This work lays the foundation for live monitoring, anomaly detection, and faster decision-making for IoT sensor networks. No major bugs fixed reported this month; focus was on delivering a robust streaming foundation and end-to-end data flow. Tech stack highlights include PySpark Structured Streaming, Kafka, and integration from data ingestion to analytics dashboards. Key commits completing the feature: - b4b6f2c656700f25cb1ac877f395eba802ab0a1a (Entregable_PAYSPARK_Jorge_Martinez) - ac05b8e2215647ad90b23478b44dc14581977fc7 (PYPARK_Jorge_Martinez_Entregable)
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 Summary: Delivered a real-time streaming data processing capability for sensor data, establishing a scalable pipeline using PySpark and Kafka that enables ingestion, processing, and analytics in near real-time. This work lays the foundation for live monitoring, anomaly detection, and faster decision-making for IoT sensor networks. No major bugs fixed reported this month; focus was on delivering a robust streaming foundation and end-to-end data flow. Tech stack highlights include PySpark Structured Streaming, Kafka, and integration from data ingestion to analytics dashboards. Key commits completing the feature: - b4b6f2c656700f25cb1ac877f395eba802ab0a1a (Entregable_PAYSPARK_Jorge_Martinez) - ac05b8e2215647ad90b23478b44dc14581977fc7 (PYPARK_Jorge_Martinez_Entregable)
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for a10pepo/EDEM_MDA2526: Delivered a new DBT Data Transformation Project for Sales & Customer Analytics, establishing standardized data transformation and analytic workflows for sales and customer metrics. Implemented models for financial analysis by brand and customer ranking based on spending, leveraging DuckDB as the in-process database to enable fast analytics. The work was shipped as a DBT project with a committed deliverable by the team member listed below, enabling data-driven decision making and faster time-to-insight.
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for a10pepo/EDEM_MDA2526: Delivered a new DBT Data Transformation Project for Sales & Customer Analytics, establishing standardized data transformation and analytic workflows for sales and customer metrics. Implemented models for financial analysis by brand and customer ranking based on spending, leveraging DuckDB as the in-process database to enable fast analytics. The work was shipped as a DBT project with a committed deliverable by the team member listed below, enabling data-driven decision making and faster time-to-insight.
November 2025
October 2025
8 Commits • 5 Features
Oct 1, 2025October 2025 performance snapshot for the a10pepo/EDEM_MDA2526 repository. Delivered five end-to-end features across scripting, containerization, and data management, and performed maintenance to improve developer onboarding and learning resources. Key outcomes include a personal README profile for Jorge Martinez with updated background and links, a Linux command line practice suite with multi-directory listing exercises, a containerized addition utility via Dockerfile and Python script, a PostgreSQL database project with employees and departments schema and queries, and a set of educational Python exercises featuring a bad-words filter. Minor README cleanups and error corrections were completed to improve clarity. The work demonstrates proficiency in Python, Docker, SQL, Linux environments, and a focus on business value by improving developer visibility, hands-on practice, and data-management capabilities.
October 2025
8 Commits • 5 Features
Oct 1, 2025October 2025 performance snapshot for the a10pepo/EDEM_MDA2526 repository. Delivered five end-to-end features across scripting, containerization, and data management, and performed maintenance to improve developer onboarding and learning resources. Key outcomes include a personal README profile for Jorge Martinez with updated background and links, a Linux command line practice suite with multi-directory listing exercises, a containerized addition utility via Dockerfile and Python script, a PostgreSQL database project with employees and departments schema and queries, and a set of educational Python exercises featuring a bad-words filter. Minor README cleanups and error corrections were completed to improve clarity. The work demonstrates proficiency in Python, Docker, SQL, Linux environments, and a focus on business value by improving developer visibility, hands-on practice, and data-management capabilities.
Activity
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Quality Metrics
Correctness93.4%
Maintainability90.0%
Architecture91.8%
Performance90.0%
AI Usage21.6%
Skills & Technologies
Programming Languages
DockerfileMarkdownPythonSQLShellYAMLbashplaintext
Technical Skills
Basic conditional logicDBTData AnalysisData EngineeringDockerDocumentationDuckDBFile handlingFile managementKafkaLinux command linePersonal BrandingProfile ManagementPySparkPython
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