Deep dives across data, automation & AI

Checkpointing and State Management in Long-Running Data Pipelines

A production-focused deep dive into building resilient data pipelines that survive failures without losing work or producing incorrect results. Learn to implement file-based and database-backed checkpointing, manage stateful aggregations in streaming pipelines, and avoid the subtle bugs that make checkpointing go wrong.

Metadata Filtering in RAG: Using Structured Attributes to Narrow Retrieval Before Vector Search

Pure vector similarity search isn't enough for production RAG systems — temporal drift, cross-domain contamination, and tenant isolation require structured filtering before or alongside embedding search. This lesson teaches you to design metadata schemas, implement pre- and post-filtering in Pinecone and Chroma, and dynamically extract filter conditions from natural language queries using an LLM.

Implementing LLM Observability: Tracing, Logging, and Monitoring Requests in Production

Most observability tools weren't built for LLMs—where a "successful" response can still be wrong, expensive, or incomplete. This lesson teaches you to build a production-grade observability stack with structured logging, distributed tracing, cost tracking, and quality monitoring across real LLM pipelines.

Integrating AI APIs into Business Workflows: Call OpenAI, Claude, and Gemini Programmatically

Stop copy-pasting outputs from browser tabs. This hands-on lesson teaches you to call OpenAI, Claude, and Gemini APIs directly from Python — with structured output, error handling, and a real ticket-classification workflow you can adapt immediately. No data engineering background required.

Fuzzy Matching and Deduplication in SQL: Using Similarity Functions and Blocking Strategies to Find Near-Duplicate Records

Exact-match SQL queries can't catch "Jon Smith" and "John Smith" as the same person. This hands-on lesson teaches you to build a complete fuzzy deduplication pipeline using similarity functions, smart blocking strategies, and composite scoring — on tables with hundreds of thousands of rows. Walk away with production-ready SQL you can actually use.

Integrating Power Automate with Microsoft Dataverse: Querying, Creating, and Updating Records in Business Process Flows

Go beyond basic drag-and-drop and build production-grade Power Automate flows that read, create, and update Dataverse records with real efficiency. This lesson covers OData filtering, lookup field handling, upsert patterns, and how to architect a complete business process flow from trigger to summary notification.

Canvas App Error Handling: Building Resilient Apps with IfError, Notify, and Graceful Failure Patterns*

Implementing Power BI Deployment Pipelines with Git Integration and Azure DevOps for Automated CI/CD of Enterprise Reports

Manual Power BI deployments are a production incident waiting to happen. This lesson walks you through building a complete CI/CD system that connects `.pbip` Git repositories, Power BI Deployment Pipelines, and Azure DevOps YAML pipelines into a fully automated, approval-gated delivery workflow. Every section includes production-ready PowerShell and YAML you can deploy today.

Building Dynamic Benchmarks in DAX: Compare Actuals Against Targets, Budgets, and Peer Groups Using SELECTEDVALUE and Virtual Relationships

Learn how to build a flexible benchmark comparison system in DAX that lets users switch between budget, company average, and peer group comparisons on the fly — without cluttering your data model. This lesson covers SELECTEDVALUE routing, TREATAS virtual relationships, and dynamic peer group calculations with real production-grade code.

Mastering Power BI Deployment Pipelines: Promoting Reports Across Development, Test, and Production Workspaces

Stop promoting Power BI reports by downloading and re-uploading .pbix files. This lesson teaches you how to build a real deployment pipeline with environment-specific configuration, selective deployment, and a team workflow that keeps development, test, and production genuinely separate. By the end, you'll have the infrastructure that separates hobbyist Power BI from enterprise Power BI.

Building Multi-Stage ETL Pipelines in Power Query M: Orchestrating Dependent Transformations with Modular Query Chains

Most Power Query pipelines collapse under their own complexity because they mix extraction, transformation, and business logic in the same queries. This lesson teaches you to architect multi-stage ETL pipelines where each query has a single job, dependencies are explicit, and the whole chain is debuggable and maintainable — the way serious production pipelines are built.

Managing Many-to-Many Relationships in Power Query: Deduplication, Bridge Tables, and Safe Merge Strategies

Many-to-many joins are the most common source of silent data corruption in Power Query — and they produce no error messages. Learn how to detect M:N relationships before they inflate your totals, and master three strategies to handle them correctly: deduplication, bridge tables, and defensive merge patterns.