DeTLeng BigQuery MCP
Experimental connection between Google BigQuery and AI through Model Context Protocol.
Why it matters: demonstrates how structured analytical data and tools can become accessible through an intelligent interface.
Repository →Projects & Innovation
Business problem → technical system → business value.
Selected work is presented as a narrative: what the project explores, why it matters and what broader capability it demonstrates.
Agentic AI
Dedicated experimentation with agentic concepts, architectures, workflows and implementation approaches.
Why it matters: explores AI as a task-oriented participant in workflows, with tools and human oversight.
Repository →GCP End-to-End ELT Data Pipeline
An end-to-end Google Cloud ELT/data-pipeline exploration.
Why it matters: connects ingestion, transformation and analytics delivery instead of treating each as an isolated exercise.
Repository →Modern SQL Pipelines
Learning and experimentation around modern SQL-based pipeline concepts.
Why it matters: turns data transformation knowledge into a documented and reusable learning asset.
Repository →Document Automation Toolkit
Reusable approaches for automating document-oriented information processing.
Why it matters: addresses repetitive knowledge work through structured workflows and reusable tooling.
Repository →Case Study AI Backend
Backend experimentation supporting AI-enabled case-study and knowledge applications.
Why it matters: demonstrates practical integration of frontend experiences, application logic and intelligent services.
Repository →Project method
Evidence before presentation
The case-study approach starts with source data and ends with documented knowledge reuse.
Business problem
→Source assessment
→Clean & transform
→Validate
→KPIs & insight
→Document & reuse
More evidence
The GitHub portfolio is the wider Innovation Lab.
Explore repositories across Azure, cybersecurity, DevOps, OpenAI applications, technical writing, communication and professional learning.