
Gemma Balaguer developed data engineering and analytics solutions in the a10pepo/EDEM_MDA2526 repository, focusing on onboarding, analytics pipelines, and real-time data processing. She implemented SQL schemas and dbt models for employee and bike-sharing data, enabling structured analytics and reporting. Using Python, Docker, and PySpark, she built containerized applications and a Kafka-based streaming pipeline to support live dashboards and prototype validation. Her work included interactive Python exercises, a CLI for social media integration, and comprehensive documentation. By decommissioning legacy streaming components, Gemma improved maintainability and aligned the codebase with evolving data architectures, demonstrating depth in backend and data engineering practices.
January 2026 monthly summary for a10pepo/EDEM_MDA2526: Focused on decommissioning legacy Kafka-based sensor data pipeline and PySpark environment as part of migrating to a new data architecture. This cleanup reduces maintenance surface, mitigates legacy risks, and accelerates adoption of the updated data platform. The work consolidates artifacts and aligns the repository with the updated architecture.
January 2026 monthly summary for a10pepo/EDEM_MDA2526: Focused on decommissioning legacy Kafka-based sensor data pipeline and PySpark environment as part of migrating to a new data architecture. This cleanup reduces maintenance surface, mitigates legacy risks, and accelerates adoption of the updated data platform. The work consolidates artifacts and aligns the repository with the updated architecture.
December 2025: Delivered foundational real-time data processing and analytics capabilities for a10pepo/EDEM_MDA2526, establishing a scalable streaming path and reusable templates that enable live dashboards, prototype validation, and data-driven decisions. Key deliverables include a PySpark-based streaming pipeline with Kafka and Dockerization, an educational notebook for hands-on learning, a CLI for Twitter/X posting with secure environment handling, and SQL-based bike-sharing analytics models. Also performed repo hygiene and stability improvements to improve reproducibility and collaboration.
December 2025: Delivered foundational real-time data processing and analytics capabilities for a10pepo/EDEM_MDA2526, establishing a scalable streaming path and reusable templates that enable live dashboards, prototype validation, and data-driven decisions. Key deliverables include a PySpark-based streaming pipeline with Kafka and Dockerization, an educational notebook for hands-on learning, a CLI for Twitter/X posting with secure environment handling, and SQL-based bike-sharing analytics models. Also performed repo hygiene and stability improvements to improve reproducibility and collaboration.
Month: 2025-11 — Focused delivery of core data capabilities and analytics pipelines for the a10pepo/EDEM_MDA2526 project. Key features delivered include an initial SQL schema and data access layer for employee data, a DB-backed Hangman game with PostgreSQL integration and API-driven word retrieval with user progress tracking, and a dbt-based analytics platform for Valenbisi bike-sharing with Dockerized deployment, data extraction, and SQL transformation models. No explicit major bugs recorded in the provided data; where issues existed, improvements were implemented in data access and project organization. Overall impact: improved data governance and analytics readiness, enabling faster business insights and more deterministic deployments. Technologies demonstrated include SQL, PostgreSQL, API integration, dbt, Docker, ETL tooling, and data modeling.
Month: 2025-11 — Focused delivery of core data capabilities and analytics pipelines for the a10pepo/EDEM_MDA2526 project. Key features delivered include an initial SQL schema and data access layer for employee data, a DB-backed Hangman game with PostgreSQL integration and API-driven word retrieval with user progress tracking, and a dbt-based analytics platform for Valenbisi bike-sharing with Dockerized deployment, data extraction, and SQL transformation models. No explicit major bugs recorded in the provided data; where issues existed, improvements were implemented in data access and project organization. Overall impact: improved data governance and analytics readiness, enabling faster business insights and more deterministic deployments. Technologies demonstrated include SQL, PostgreSQL, API integration, dbt, Docker, ETL tooling, and data modeling.
Monthly summary for 2025-10 focused on delivering structured student onboarding, Linux-focused practice materials, containerized deployments, and hands-on Python exercises. Work emphasizes business value through clear onboarding artifacts, practical system skills, and scalable deployment patterns.
Monthly summary for 2025-10 focused on delivering structured student onboarding, Linux-focused practice materials, containerized deployments, and hands-on Python exercises. Work emphasizes business value through clear onboarding artifacts, practical system skills, and scalable deployment patterns.

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