The Curious Engineer

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Data Engineering Blog & Second Brain

ssp.sh

Data Engineering Blog with genuine insights by Simon Späti: 100+ open-source data essays, a 1,000+ note Second Brain, and the book Patterns of Data Engineering.

DatabasesSoftware / engineering practiceDev tools / editors / IDEsEssays / personal

’15’16’17’18’19’20’21’22’23’24’25’26
Posts
130 from 19 Sep 2015 to 25 Oct 2026
Posting
weekly (42 in the last year)
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personal blog
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Also at
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2026 34

  1. Second Brain
  2. Data Engineering Is the Cache Invalidation Problem Ft. Josh Wills
  3. Why We Need Yet Another (OSS) Semantic Layer
  4. A Year of Linux, a Month of Omarchy Quattro—Part 3
  5. Obsidian Vault to Enterprise Company Brain: Where Agents Write, Branch and Merge
  6. The Grammar of Data: From Definition to Execution
  7. Context, Semantics, and Ontology: A Primer for the Agentic Era
  8. Memoriesphotos and moments since 2004, in Flickr and Instagram style
  9. If You Always Enjoy It, You're Not Pushing It Hard Enough—Benn Stancil (Show Your Workflow #1)
  10. Writing really is an Emotional Rollercoaster
  11. Visual Changelogevery design change to this site, visually documented
  12. Show Your Workflowinterviews on how people write and come up with ideas
  13. Figma for Agents: How Airflow's Creator Coordinates AI ft. Maxime Beauchemin
  14. Book Recommendations and Notes
  15. The Act and the Outcome of Creation
  16. The Grammar of Data
  17. Where AI Agents Belong in Data Engineering
  18. The Process of Smart Note-Taking
  19. Operationalizing Data Orchestration
  20. Beyond the Semantic Layer
  21. Vibe Coding Is Dangerous, Agentic Engineering Isn't
  22. The Dagster Almanack: From Complexity to Composability
  23. Interview with Chris Riccomini
  24. Internal vs. External Storage? What's the Limit of External Tables
  25. AI Use
  26. AI Reveals Why BI Still Matters
  27. Specs Over Vibes: Consistent AI Results ft. Mark Freeman
  28. Building an Agent-Friendly, Local-First Analytics Stack with MotherDuck and Rill
  29. Ten Years of Writing in Public: Why I still Blog
  30. Git for Data Applied: Comparing Git-like Tools That Separate Metadata from Data
  31. Building an Obsidian RAG with DuckDB and MotherDuck
  32. Arch Linux (Omarchy) — 8 Months Later: The Good, the Bad, and the Fixable
  33. Why Coinbase and Pinterest Chose StarRocks: Lakehouse-Native Design and Fast Joins at Terabyte Scale
  34. A Diary of a Data Engineer

2025 35

  1. Well Being in Times of Algorithms
  2. Opinionated Data Platforms vs. Open-Source: The Chef’s Choice for Your Data Platform
  3. Simplicity of a Database, but the Speed of a Cache: OLAP Caches for DuckDB
  4. Dlt+ClickHouse+Rill: Multi-Cloud Cost Analytics, Cloud-Ready
  5. Branch, Test, Deploy: A Git-Inspired Approach for Data
  6. Multi-Cloud Cost Analytics: From Cost-Export to Parquet to Rill
  7. Boredom is the New Luxury
  8. 4 Senior Data Engineers Answer 10 Top Reddit Questions
  9. Data Modeling for the Agentic Era: Semantics, Speed, and Stewardship
  10. Beyond Basic ETL: Enterprise Data Capabilities Without the Complexity
  11. Why I Don't Research; and Write from Experience
  12. Data Modeling Guide for Real-Time Analytics with ClickHouse
  13. Writer's Room
  14. Media Kit
  15. Why Semantic Layers Matter—and How to Build One with DuckDB
  16. My Journey from macOS to Arch Linux with Omarchy
  17. Summer Data Engineering Roadmap
  18. Why Are We Here on Earth? True Happiness, Giving Up Control, or the Trap of Worshipping Earthly Things?
  19. The Data Engineering Toolkit: Infrastructure, DevOps, and Beyond
  20. Has Self-Serve BI Finally Arrived Thanks to AI?
  21. Impressum privacy
  22. Universal Data Orchestrator in Action: Enterprise Best Practices
  23. The Open Lakehouse Stack: DuckDB and the Rise of Table Formats
  24. Self-Host & Tech Independence: The Joy of Building Your Own
  25. The Open Table Format Revolution: Why Hyperscalers Are Betting on Managed Iceberg
  26. Configure, Don't Code: How Declarative Data Stacks Enable Enterprise Scale
  27. The Data Engineer's Guide to Efficient Log Parsing with DuckDB/MotherDuck
  28. The Universal Data Orchestrator: The Heartbeat of Data Engineering
  29. What «Shifting Left» Means and Why it Matters for Data Stacks
  30. Vector Technologies for AI: Extending Your Existing Data Stack
  31. Scaling Beyond Postgres: How to Choose a Real-Time Analytical Database
  32. A Beginner’s Guide to Geospatial with DuckDB
  33. Finding Flow: Escaping Digital Distractions Through Deep Work and Slow Living
  34. Why Pivot Tables Never Die
  35. The Data Engineering Toolkit: Essential Tools for Your Machine

2024 14

  1. Designing a Declarative Data Stack: From Theory to Practice
  2. Semantic Layer and AI: The Future of Data Querying with Natural Language
  3. Universal Semantic Layer: Capabilities, Integrations, and Enterprise Benefits
  4. Building with Bluesky: Inside the New Open Social Network
  5. Exploring the Semantic Layer Through the Lens of MVC
  6. 15+ Companies Using DuckDB in Production: A Comprehensive Guide
  7. BI-as-Code and the New Era of GenBI
  8. The Enterprise Case for DuckDB: 5 Key Categories and Why Use It
  9. The Rise of the Declarative Data Stack
  10. My Crafthow I write data engineering content, including my principles
  11. All Rights Reserved
  12. Free Checklist: Increase Reach Through Writing
  13. My Services
  14. My Obsidian Note-Taking Workflow

2023 10

  1. The Plain Text Workflow: How Vim and Markdown Became My Backbone
  2. Patterns of Data Engineering
  3. Data Modeling - The Unsung Hero of Data Engineering: Architecture Pattern, Tools and the Future (Part 3)
  4. Data Modeling – The Unsung Hero of Data Engineering: Modeling Approaches and Techniques (Part 2)
  5. Data Modeling – The Unsung Hero of Data Engineering: An Introduction to Data Modeling (Part 1)
  6. Pandas 2.0 and its Ecosystem (Arrow, Polars, DuckDB)
  7. Finding My Pathless Path
  8. Modern Data Stack: The Struggle of Enterprise Adoption
  9. Why Vim Is More than Just an Editor – Vim Language, Motions, and Modes Explained
  10. The Open Data Stack Distilled into Four Core Tools

2022 7

  1. Data Integration as Code: Configuring Airbyte and dbt with Python (Dagster)
  2. Rust for Data Engineering
  3. The Rise of the Semantic Layer
  4. Data Lake / Lakehouse Guide: Powered by Data Lake Table Formats (Delta Lake, Iceberg, Hudi)
  5. Data Orchestration Trends: The Shift From Data Pipelines to Data Products
  6. Personal Knowledge Management Workflow for a Deeper Life — as a Computer Scientist
  7. Building an Analytics API with GraphQL: The Next Level of Data Engineering?

2021 3

  1. How to Take Notes in 2021?
  2. Saying Goodbye to WordPress, a Homecoming
  3. Building a Data Engineering Project in 20 Minutes

2020 1

  1. Business Intelligence meets Data Engineering with Emerging Technologies

2019 1

  1. Email, and the way we (should) communicate at work

2018 5

  1. Open-Source Data Warehousing – Druid, Apache Airflow & Superset
  2. OLAP, what’s coming next?
  3. Today’s Office – The Location Independent Lifestyle
  4. Tools I Use – Part III
  5. Data Engineering, the future of Data Warehousing?

2017 8

  1. Data Warehouse vs Data Lake | ETL vs ELT
  2. Tools I Use – Microsoft OneNote – Part II
  3. What Data Warehouse Automation tools are on the market
  4. Why automate? What does Data Warehouse Automation for us?
  5. Why Data Warehouse Automation is not more popular
  6. Data Warehouse Automation (DWA) – Series
  7. Tools I Use
  8. The more you share the more you get..

2016 3

  1. Migrate from Oracle to Microsoft (Views) – Part III
  2. Migrate from Oracle to Microsoft (Views) – Part II
  3. Migrate from Oracle to Microsoft (Views) – Part I

2015 5

  1. SSAS Cubes – Dynamic generation of partition
  2. Need any Headphones?
  3. Do you listen to music while working?
  4. Annoying advertising, what you can do
  5. ORDER BY Oracle vs. Microsoft SQL

Undated 4

  1. Data Engineering Blog
  2. Years
  3. Hacker News Front Page Hits
  4. Reddit Posts