1 min read
Fabric Data Factory Notes: designing pipelines for failure, not the happy path
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
Fabric
44 articles
1 min read
OneLake Discipline: building discoverability into workspace design
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Designing Better Lakehouse Flows in Fabric: turning messy raw zones into reliable products
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
Streaming Design Notes in Fabric: keeping streaming dashboards useful after week one
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Fabric Warehouse Tradeoffs: building models analysts can trust without constant hand-holding
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Orchestration Lessons in Fabric: debugging pipeline latency before it becomes a fire drill
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
OneLake Discipline: why governance has to be designed before scale
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Designing Better Lakehouse Flows in Fabric: why table contracts matter before notebooks scale
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Real-Time Intelligence in Fabric: aligning action thresholds with business impact
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Making Fabric Warehouse Boring and Reliable: when I use warehouse tables vs lakehouse shortcuts
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Orchestration Lessons in Fabric: how I keep orchestration readable over time
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
OneLake Shortcuts in Practice: balancing speed and access boundaries
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Fabric Architecture Notes: where I separate ingestion from curation
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
Streaming Design Notes in Fabric: using KQL to separate noise from meaningful changes
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Warehouse Modeling in Fabric: preventing metric drift through ownership rules
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
Orchestration Lessons in Fabric: debugging pipeline latency before it becomes a fire drill
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
OneLake Shortcuts in Practice: why governance has to be designed before scale
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Fabric Lakehouse Patterns: turning messy raw zones into reliable products
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
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KQL and Operational Awareness: using KQL to separate noise from meaningful changes
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Warehouse Modeling in Fabric: preventing metric drift through ownership rules
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Orchestration Lessons in Fabric: designing pipelines for failure, not the happy path
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Keeping OneLake Clean Under Delivery Pressure: why governance has to be designed before scale
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Fabric Lakehouse Patterns: turning messy raw zones into reliable products
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Real-Time Signals That Actually Help: building alerting that avoids fatigue
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
SQL in Fabric: Working Notes: building models analysts can trust without constant hand-holding
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Data Movement Without Drama: designing pipelines for failure, not the happy path
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Keeping OneLake Clean Under Delivery Pressure: balancing speed and access boundaries
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Fabric Architecture Notes: turning messy raw zones into reliable products
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Real-Time Signals That Actually Help: keeping streaming dashboards useful after week one
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Fabric Warehouse Tradeoffs: choosing model grain before performance tuning
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
1 min read
Pipelines I Trust in Fabric: reducing brittle dependencies in daily loads
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Keeping OneLake Clean Under Delivery Pressure: why governance has to be designed before scale
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Lakehouse Decisions I Made This Week: turning messy raw zones into reliable products
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
3 min read
Real-Time Intelligence in Microsoft Fabric: A Working Example
Here's what it is, how it works, and a pattern that's running in production. Traditional analytics is batch-oriented. Data lands in storage, gets processed…
2 min read
KQL: The Query Language You're Probably Ignoring
That's a missed opportunity. Kusto Query Language. Developed by Microsoft. Used across Azure Monitor, Azure Data Explorer, Microsoft Sentinel, and now…
2 min read
Delta Lake in Microsoft Fabric: Why the Format Matters
When I tell clients "everything in Fabric uses Delta Lake format," the room divides. Data engineers nod. Everyone else says "what?"
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Microsoft Fabric: Six Months In Production
It worked, but managing five services with five billing models and five sets of credentials was painful.
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Data Pipelines: What Production Actually Teaches You
I've built data pipelines for 8 years. Every production deployment humbled me. Here's what the tutorials don't teach.
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Migrating to Fabric: 5 Things I Wish I Knew
We migrated from Synapse to Fabric. It took twice as long as expected. Here's what I wish someone had told me.
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Fabric Capacity Planning: Lessons from Production
Started with F64 because "enterprise." Spent money we didn't need to. Lesson: Start smaller. F32 was enough. Can always scale up.
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Databricks vs Fabric: Which Should You Choose?
Clients ask me this weekly: "Should I use Databricks or Fabric?" My answer: It depends. Let me explain.
3 min read
Microsoft Fabric: Six Months In Production
OneLake is brilliant. One storage layer for everything. No more copying data between Synapse, Data Lake, and Power BI. It just works.
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Top 10 Azure Data Services Lessons from 2025
After a year of implementing data solutions on Azure, I've compiled the most important lessons that can save you time, money, and headaches. These come from…
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2023 AI Year in Review: Microsoft's AI Transformation (Part 2)
Microsoft's 2023 felt like an all-in moment: platform bets, product integration, and commercial commitments that reshaped the enterprise AI landscape. From…