Practical Tutorials,
Production-Grade Code.
Deep-dive technical walkthroughs, reproducible code recipes, and architecture patterns across Python, SQL, Power BI, and Agentic AI.
Showing all 12 technical tutorials
Building an Autonomous SQL Analyst Agent with LangGraph & DuckDB
Learn how to construct a cyclic agent workflow that autonomously navigates complex relational schemas, verifies read-only security permissions, executes analytical queries, and self-corrects SQL syntax failures.
class AgentState(TypedDict):
question: str
schema: str
sql_query: str
query_result: list
error: str | None
iterations: int
# Cyclic Self-Correction State Graph
workflow = StateGraph(AgentState)
workflow.add_node("generator", generate_sql)
workflow.add_node("executor", execute_query)
workflow.add_conditional_edges(
"executor",
should_retry,
"retry": "generator", "done": END
)All Tutorials & Hands-on Labs.
Click on any guide to view prerequisites, complete architecture breakdowns, and full code implementations.
Building an Autonomous SQL Data Analyst Agent with LangGraph & DuckDB
Construct a multi-agent system that connects to local databases, dynamically inspects schemas, generates safe read-only SQL, self-heals execution errors, and synthesizes executive charts.
- Implement cyclic state graphs with error-correction feedback loops
- Enforce read-only safety barriers and prevent destructive SQL operations
- Auto-generate Matplotlib/Seaborn visualizations from executed query results
Diagnosing & Optimizing Sluggish DAX with DAX Studio & VertiPaq Analyzer
A step-by-step diagnostic workflow to pinpoint storage engine vs. formula engine bottlenecks, eliminate expensive callback loops, and rewrite CALCULATE expressions for sub-second visual rendering.
- Read VertiPaq server timings: xmSQL queries vs. single-threaded SE/FE execution
- Eliminate CallbackDataID bottlenecks caused by non-vectorized DAX functions
- Optimize high-cardinality relationships and memory footprint
Advanced SQL Window Functions for SaaS Cohort & Retention Analytics
Master LEAD, LAG, ROW_NUMBER, and custom rolling window frames to calculate customer churn, MoM expansion ARR, and 30-day active retention curves on real PostgreSQL schemas.
- Calculate user retention matrices and cohort survival rates purely in SQL
- Distinguish ROWS BETWEEN and RANGE BETWEEN for date gaps and edge cases
- Avoid expensive self-joins by leveraging ordered analytic partitions
High-Throughput ETL Pipelines with Python, Polars & DuckDB
Ditch memory-heavy Pandas scripts. Learn how to combine Polars lazy evaluation with DuckDB to process 25M+ rows of messy financial transaction files in sub-5 seconds on a laptop.
- Write optimized query plans using Polars LazyFrame expressions and streaming mode
- Query directly across gigabytes of uncompressed CSVs and Parquet with DuckDB
- Cut pipeline memory overhead by over 80% without cloud infrastructure costs
Enterprise Star Schema Architecture: Multi-Grain Facts & Bridge Tables
The architectural blueprint for clean Power BI models: resolving many-to-many relationships with bridge tables, handling sales targets at different grains, and eliminating bidirectional filter traps.
- Design clean 1-to-many star schemas that scale to hundreds of millions of records
- Resolve mixed-grain budgets vs daily transaction facts cleanly
- Avoid bidirectional relationship pitfalls that cause silent calculation corruption
Deploying Scikit-Learn Predictive Models with FastAPI & Docker
Transform a trained machine learning model artifact (.joblib) into a production microservice featuring Pydantic schema validation, structured error handling, automated tests, and Docker containerization.
- Structure high-performance asynchronous API endpoints with FastAPI
- Strict input payload validation with Pydantic v2 schemas and sanitization
- Build multi-stage lightweight Docker images under 150MB with automated health checks
Mastering Recursive SQL Queries: Hierarchies & Parent-Child Trees
Use WITH RECURSIVE Common Table Expressions to query organization employee trees, multi-tier bill-of-materials, and breadcrumb product category paths without looping in application code.
- Understand the 2 components of recursive queries: Anchor member and Recursive member
- Compute hierarchy depth, full lineage path strings, and subtree aggregates
- Prevent infinite recursion cycles in data with cycle detection flags
Production RAG with Hybrid Dense-Sparse Search & Cohere Reranking
Overcome naive vector search failures. Build a production-grade Retrieval-Augmented Generation pipeline combining BM25 keyword matching, vector embeddings, and cross-encoder reranking.
- Solve semantic vector blind spots for exact part numbers, codes, and acronyms
- Implement Reciprocal Rank Fusion (RRF) to merge keyword and dense rankings
- Drastically reduce token costs and hallucinations with Cohere / cross-encoder rerankers
Automated Headless Web Scraping with Playwright & Python
Extract data from dynamic, JavaScript-rendered websites. Learn how to handle infinite scrolling, solve anti-bot challenges, wait for network idle, and stream clean structured JSON directly to disk.
- Automate Chromium headless sessions with async Playwright in Python
- Intercept and parse background XHR/Fetch API responses directly
- Implement robust retry strategies and exponential backoff for flaky endpoints
Machine Learning Evaluation: Precision-Recall, ROC-AUC & Cost Matrices
Why 99% accuracy is often useless in real-world fraud and churn modeling. Learn how to calibrate classification probability thresholds and align model metrics with dollar business costs.
- Choose between Precision-Recall curves and ROC-AUC for imbalanced datasets
- Optimize decision thresholds to minimize business dollar loss rather than 0.5 default
- Calculate Expected Value matrices for risk management teams
Implementing Dynamic Row-Level Security (RLS) in Power BI Service
Set up bulletproof security filters so users only see their authorized region or subsidiary. Use USERPRINCIPALNAME, organizational hierarchy tables, and test role impersonation.
- Write dynamic DAX security rules using USERPRINCIPALNAME() and PATHCONTAINS()
- Implement managerial hierarchy roll-ups so managers see all direct report data
- Validate security filters safely using Test as Role in Power BI Desktop & Service
Lightweight Event Ingestion & Analytics with DuckDB & Webhooks
Build an embedded real-time event sink. Ingest webhook event payloads, buffer incoming JSON transactions in memory, and query live analytics with zero external database server setup.
- Buffer micro-batches of webhook JSON payloads using Python async queues
- Direct high-speed append into DuckDB columnar storage format
- Query real-time metrics with analytical window functions without Redis or Kafka overhead
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Guided tracks for specialized roles.
Structured sequences to take you step-by-step through a complete engineering capability.
Modern BI Architecture Track
Transform raw, disorganized databases into reliable, sub-second executive reporting suites.
- 1 Enterprise Star Schema Architecture
- 2 Diagnosing & Optimizing Sluggish DAX
- 3 Implementing Dynamic Row-Level Security
Data Automation & Engineering Track
Eliminate manual spreadsheet work and build lightning-fast, automated pipelines with modern Python tooling.
- 1 High-Throughput ETL with Polars & DuckDB
- 2 Automated Headless Web Scraping with Playwright
- 3 Lightweight Event Ingestion with DuckDB
Applied AI & Autonomous Agents Track
Graduate beyond toy prompts into deterministic cyclic state machines, production RAG, and microservice deployment.
- 1 Autonomous SQL Data Analyst Agent with LangGraph
- 2 Production RAG with Hybrid Dense-Sparse Search
- 3 Deploying ML Models with FastAPI & Docker
Need a Custom Lab or 1-on-1 Code Review?
If you are struggling with a specific DAX formula, slow SQL query plan, or LangGraph state machine, let's pair program and solve it together.