How to design reliable AI-agent systems using harness engineering, skills, shell execution, and context compaction. A practical tutorial with real examples for OpenAI Codex and Claude Code ecosystems.
A hands-on, zero-nonsense plan to learn Rust with precision: ownership, traits, async, Arrow, Polars, DataFusion, web services, FFI, testing, profiling, and shipping. Includes a weekly rhythm and a realistic scope for busy data engineers.
Deep-dive guide to effect polymorphism in Scala using F[_] and type classes. Write generic code that works with Cats-Effect or ZIO, then swap runtimes later. Learn EffectSystem patterns, Kleisli composition, and examples from flowforge.
Learn Kleisli from first principles to compose effectful data pipelines with Cats or Cats Effect, wiring sources, transforms, and sinks with clear errors, observability, and easy tests. Includes a Scala example you can lift into prod.
Build Scala compile-time data contracts with macros and TypeInspector patterns so schema drift fails at compile time, not midnight in prod. Includes patterns for required vs optional fields and compile errors that block bad data early.
Deep-dive into building a Scala SDK for CDC at petabyte scale on GCP. Learn partition pruning, getAffectedPartitions, cloud-native backups, and CDC without ACID. Includes metrics, scan reduction stats, and code from Delta/Hive lakes.
Complete guide to building a data quality framework. Learn constraint validation in Scala, YAML checks (DPAT), Spark/Airflow/CI/CD integration, and automated failure reporting. Includes real code and architecture to catch issues early.
Build reusable Scala utilities for data engineering on GCP, covering CDC, Delta maintenance, schema evolution, GCS helpers, affected-partition strategies, and error handling. Includes patterns for safe ops and repeatable pipelines.
Production-tested strategy to cut pipeline failures from 12% to 2% using YAML-based data quality checks (DPAT), CI/CD gates, and strict data contracts. Includes before/after metrics and architecture for self-healing pipelines in prod.
Deep-dive guide to building data-centric backends with Kotlin. Learn pipeline patterns with coroutines, Spark/Flink integration, LLM enrichment, lakehouse patterns (Iceberg/Delta), and observability with OpenTelemetry. Includes real code.
Guide to building AI pipelines with BigQuery ML and Vertex AI. Learn SQL-first modeling, real-time endpoints, automated retraining, and drift monitoring. Includes diagrams and code that cut time-to-model from 6 weeks to 2 hours fast.
Building a demand forecasting system with Random Forest, ANN, and hybrid time-series models on Hadoop and Spark. Covers data ingestion, feature engineering, model training, validation, and rollout for large SKU catalogs and seasonality.