Deep dives across data, automation & AI

Reranking Retrieved Results: Implementing Cross-Encoders to Improve RAG Accuracy

Your RAG pipeline's retrieval quality determines everything downstream — and bi-encoder vector search has a fundamental limitation that reranking solves. Learn how to implement a production-grade cross-encoder reranking stage that measurably improves answer accuracy without rebuilding your pipeline from scratch.

Implementing Hybrid Search for RAG: Combining Dense and Sparse Retrieval

Dense vector search misses exact matches; BM25 misses synonyms and paraphrase. This practical lesson shows you how to combine both into a hybrid RAG pipeline using Reciprocal Rank Fusion — with complete, production-ready Python code and tuning guidance.

Automating Repetitive Reporting Workflows with AI: From Data Summaries to Stakeholder-Ready Narratives

Stop spending your afternoons translating dashboard numbers into Word documents. This hands-on lesson walks you through building a production-ready Python pipeline that takes raw data, computes structured summaries, and uses AI to generate consistent, professional narrative reports — automatically, every reporting cycle.

Temporal Data Mastery: Writing Queries for Time-Series, Date Ranges, and Slowly Changing Dimensions

Temporal data is where SQL skills separate practitioners from experts. This lesson teaches you to write production-quality queries for time-series aggregations, gap-filling, point-in-time lookups, and Slowly Changing Dimensions — the four categories of temporal problems every serious data team faces. Leave with patterns you can apply immediately.

Scheduling and Managing Time-Based Flows in Power Automate: Recurrence Triggers, Time Zones, and Business Hours Logic*

Power Apps Performance Optimization: Delegation, Data Row Limits, and Reducing App Load Times

Delegation errors don't crash your app — they silently return wrong data, and most developers don't catch them until production. This deep-dive lesson teaches you to diagnose delegation issues, rewrite non-delegable formulas, and cut app load times using Concurrent(), named formulas, and strategic collections.

Designing and Implementing Power BI Incremental Refresh for Large-Scale Enterprise Datasets

Stop waiting hours for Power BI dataset refreshes to complete. This deep-dive lesson walks you through designing and implementing production-grade incremental refresh for enterprise datasets, from query folding fundamentals to XMLA partition management — with realistic code examples and troubleshooting you won't find in the official docs.

Mastering DAX Security: Dynamic Row-Level Security with USERPRINCIPALNAME and Org Hierarchies

Static RLS roles break the moment your org chart changes. Learn how to build a production-grade dynamic RLS system using USERPRINCIPALNAME, PATH functions, and a security bridge table that enforces manager-subordinate hierarchy automatically — no manual maintenance required.

Implementing Custom Query Folding Logic in M: Keeping Transformations Native to the Data Source

Query folding can mean the difference between a 2-minute refresh and a 25-minute timeout on large datasets. This lesson teaches you to diagnose folding breaks, restructure queries to preserve them, and use Value.NativeQuery for precise native SQL control — with production-ready patterns throughout.

Automating Incremental Data Refreshes in Power Query with Persistent State and Change Tracking

Full refreshes at scale are a performance disaster — but Power Query gives you the tools to build watermark-based incremental pipelines that only process what's actually changed. This lesson walks through the complete architecture: persistent state, upsert merging, late-arrival handling, and failure recovery.

Writing a Winning Data Freelance Proposal: Structure, Messaging, and Common Mistakes to Avoid

Most data freelancers lose projects not because their skills are weak, but because their proposals read like resumes instead of business cases. This lesson teaches you the exact structure, messaging approach, and client psychology you need to write proposals that consistently win.

Slowly Changing Dimensions in Practice: Handling Historical Data Changes in Your Warehouse

When a customer moves cities or an employee gets promoted, does your data warehouse know what was true at the time of each transaction? This lesson teaches you exactly how to design, implement, and query SCD Type 1, 2, and 3 tables so your historical reports are always accurate.