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Arch Linux (Omarchy) — 8 Months Later: The Good, the Bad, and the Fixable
Programmation IA

Arch Linux (Omarchy) — 8 Months Later: The Good, the Bad, and the Fixable

This is a follow-up to my part 1 of Switching macOS to Arch Linux with Omarchy, where I documented my first months with Arch Linux and [[Omarchy]], after switching from 15 years of using macOS and Windows on and off at work since 2003. Back then, I had a checklist of basics I needed before I could commit to Linux as a daily driver: Obsidian, a Raycast-like launcher for fuzzy finding files and folders, screenshots (Snagit), daylight adjustment (f.lux), calendar events in the top bar. Those were…

SSP Data Engineering Blog
Why Coinbase and Pinterest Chose StarRocks: Lakehouse-Native Design and Fast Joins at Terabyte Scale
Programmation IA

Why Coinbase and Pinterest Chose StarRocks: Lakehouse-Native Design and Fast Joins at Terabyte Scale

Why is StarRocks gaining popularity among data engineers who need fast analytics on large-scale data? To find out, I did a deep dive on the companies actually using StarRocks in production, interviewing engineers and studying technical case studies from Coinbase, Pinterest, Fresha, Grab, TRM Labs, and Shopee. They all share a similar pattern: customer-facing analytics on Snowflake got too slow, and they needed sub-second query responses without heavy pre-denormalization in Flink or Spark.

SSP Data Engineering Blog
A Diary of a Data Engineer
Programmation IA

A Diary of a Data Engineer

You ingest data. You model it. You transform it. You serve it. Someone asks for a change. Everything breaks. You rebuild. This is the loop. It was the loop in 2005 with SSIS and star schemas. It’s the loop in 2025 with dbt and Iceberg, or 2026 with prompting AI agents. The tools change. The loop doesn’t. The Invisible Plumbers When I started my career in 2003, there was no “data engineering”. There was no big data, no data science. We called it Business Intelligence. Data Warehouse Developer.…

SSP Data Engineering Blog
Information-Driven Design of Imaging Systems
IA

Information-Driven Design of Imaging Systems

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require…

BAIR Blog
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