Welcome to eat·sleep·code.dev — a digital workbench exploring the intersection of modern data engineering, distributed analytics, and agentic AI systems.
What to Expect#
In this space, we dive into practical engineering insights, enterprise architecture patterns, and the hard-learned lessons that only surface in production:
- Microsoft Fabric & Lakehouse Architecture: Medallion design patterns, Delta Lake internals, Direct Lake performance tuning, and unified governance.
- Multi-Agent AI Systems: Moving beyond simple chat prompts into autonomous, resilient multi-agent execution graphs for enterprise data operations.
- PySpark & High-Throughput Pipelines: Distributed computation optimization, memory management, shuffle tuning, and scalable ETL/ELT pipelines.
- System Design & Engineering Principles: Architectural decisions, API designs, and building robust, maintainable cloud platforms.
# Multi-Agent Execution Loop in Python
async def run_data_agent(task: str, context: dict) -> AgentResult:
plan = await orchestrator.plan_step(task, context)
for step in plan.steps:
result = await step.execute()
if not result.success:
return await orchestrator.remedy(step, result)
return AgentResult(status="SUCCESS", payload=plan.summary)Stay tuned for deep-dives, architectural blueprints, and code walkthroughs.