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Oct 20

Day 0–100 Days of Polars: The Plan

A pandas user’s 100-post plan to learn Polars properly: expressions, lazy plans and production pipelines, all on real data.

Day 0–100 Days of Polars: The Plan

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1d ago

An LLM Call Is Just a Stateless API

Pipelines to Prompts — What a data engineer needs to know about tokens, cost and failure

An LLM Call Is Just a Stateless API

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Python in Plain English

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3d ago

Day 8–12 pandas Habits to Unlearn (A Cheat Sheet)

100 Days of Polars · Phase 1: Foundations

Day 8–12 pandas Habits to Unlearn (A Cheat Sheet)

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Python in Plain English

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4d ago

Day 7 — Eager vs Lazy in One Picture

100 Days of Polars · Phase 1: Foundations

Day 7 — Eager vs Lazy in One Picture

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Towards Data Engineering

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Double Espresso Data

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4d ago

From SQL Analyst to Analytics Engineer (Part 6B): Dissecting Native Fabric MERGE T-SQL & Production…

Inspect compiled MERGE queries line-by-line, master merge_exclude_columns for audit history, conquer late-arriving data with lookback…

From SQL Analyst to Analytics Engineer (Part 6B): Dissecting Native Fabric MERGE T-SQL & Production…

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Python in Plain English

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5d ago

Day 6 — The Four Contexts Where Every Polars Expression Lives

100 Days of Polars · Phase 1: Foundations

Day 6 — The Four Contexts Where Every Polars Expression Lives

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Towards Data Engineering

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Double Espresso Data

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5d ago

From SQL Analyst to Analytics Engineer (Part 4): The Kimball Star Schema and Power BI Direct Lake…

Bridging enterprise dimensional modeling with Microsoft Fabric’s lightning-fast Direct Lake mode, eliminating BI spaghetti SQL, and mapping…

From SQL Analyst to Analytics Engineer (Part 4): The Kimball Star Schema and Power BI Direct Lake…

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Towards Data Engineering

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Double Espresso Data

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6d ago

From SQL Analyst to Analytics Engineer (Part 6A): The What & Why of Incremental Loading in…

Overcome the Big Data Wall, master the 3 conditions of is_incremental(), understand native atomic MERGE vs delete+insert, and build your…

From SQL Analyst to Analytics Engineer (Part 6A): The What & Why of Incremental Loading in…

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Python in Plain English

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Double Espresso Data

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6d ago

Day 5 — read vs scan: The Most Important Choice You Make Before Any Code Runs

100 Days of Polars · Phase 1: Foundations

Day 5 — read vs scan: The Most Important Choice You Make Before Any Code Runs

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Python in Plain English

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Oct 29

Day 4 — Series and DataFrame: Strict Types, No Index

100 Days of Polars · Phase 1: Foundations

Day 4 — Series and DataFrame: Strict Types, No Index

Double Espresso Data

Double Espresso Data

Practical Data Engineering. Actionable SQL & Python tips, Pandas vs Polars vs DuckDB breakdowns, and guides to BI semantic layers.

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