Deep dives across data, automation & AI

Turning a Completed Freelance Project into a Client Case Study That Attracts Inbound Leads

Most data freelancers finish a project and move on, leaving one of their most powerful marketing assets untouched. This lesson teaches you how to turn any completed engagement into a structured case study that generates warm inbound leads — covering narrative structure, confidentiality, publishing strategy, and how to activate it throughout your sales process.

Implementing dbt Tests and Data Quality Checks in Production Pipelines

Most dbt projects have tests. Few have a data quality strategy. This lesson takes you from basic generic tests to custom macros, statistical checks, and a tiered execution strategy that scales to production without grinding your pipeline to a halt.

Secrets Management and Credential Rotation for Data Pipelines in Production

Learn how to eliminate hardcoded credentials from your data pipelines and implement zero-downtime credential rotation using AWS Secrets Manager and HashiCorp Vault. This hands-on lesson covers everything from building a caching secrets client to writing your own rotation Lambda — with complete, production-ready Python code.

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.