Prefect 3 in Python: Flows, Deployments, Work Pools, and Transactions for Modern Data Pipelines (2026)
Prefect 3.8 in Python 2026: hands-on guide to flows, deployments, work pools, automations, and transactions for reliable data pipelines.
Tutorials
Step-by-step learning guides
Airflow 3 in Python: TaskFlow, Task Groups, and Dynamic Task Mapping for Modern Data Pipelines (2026)
A hands-on tour of Apache Airflow 3.3 with the airflow.sdk Task SDK: TaskFlow decorators, task groups, dynamic task mapping with .expand(), assets, native DAG versioning, and a 2.x migration playbook drawn from moving 900 DAGs.
dbt Unit Testing in 2026: A Practical Guide to Testing SQL Models Before They Hit Production
A practical 2026 guide to dbt unit tests: given/expect syntax, dict/csv/sql fixture formats, testing incremental models and macros, and running the suite in CI on DuckDB.
LLM Quantization in Python: GPTQ vs AWQ vs bitsandbytes vs GGUF (2026)
Compare GPTQ, AWQ, bitsandbytes, and GGUF for LLM quantization in Python. Real H100 benchmarks, kernel choices, and a production-ready decision tree for 2026.
Geospatial Analysis with GeoPandas 1.0 in Python: A Practical 2026 Guide
A practical 2026 walkthrough of GeoPandas 1.0 for Python geospatial analysis: installing the stack, handling CRS gotchas, running spatial joins, plotting interactive maps, and scaling beyond memory with DuckDB Spatial and GeoParquet.
LLM Inference Servers in Python: vLLM vs TGI vs SGLang Compared (2026)
Compare vLLM, TGI, and SGLang for serving LLMs on your own GPUs in 2026. Throughput numbers, prefix caching, quantization tradeoffs, and a production Docker deployment with monitoring.
Causal Inference in Python: DoWhy, EconML, and CausalML Compared (2026)
A 2026 guide to causal inference in Python: when to pick DoWhy, EconML, or CausalML, with runnable code, refutation tests, and the pitfalls that bite real projects.
Async ETL in Python: httpx + asyncio to Pandas DataFrames (2026)
Build async ETL pipelines in Python using httpx and asyncio.TaskGroup with Pydantic V2 validation. Working code, retry policy, rate-limit handling, and the pitfalls that bite in production.
ML Experiment Tracking in Python: MLflow vs W&B vs Comet (2026)
A 2026 benchmark of MLflow 3.0, Weights & Biases, and Comet for Python ML experiment tracking, with code, a feature matrix, and a clear decision tree.
Polars apply alternative: when/then/otherwise for 80x speedups
Eight worked examples that swap pandas-style .apply() in Polars for native when/then/otherwise expressions, plus benchmarks showing the real speedup on a 50M-row dataset.
dbt Incremental Model Not Working? The 5 Silent Full-Refresh Traps
A field-tested walkthrough of the five reasons your dbt incremental model silently runs a full refresh, with a reproducible debug recipe using compiled SQL, run results, and warehouse query history.
Polars vs pandas in 2026: a real 14GB benchmark, not a toy CSV
Every Polars-vs-pandas comparison I've read in the last year uses a 500MB CSV and announces that Polars is faster. I wanted to know what happens on the workload I actually run at work.