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Data Engineering

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Data Governance is Dead, Long Live AI Readiness

AI infrastructure

Featured

Data Governance is Dead, Long Live AI Readiness

Why traditional data governance frameworks are collapsing under AI’s weight, and what actually needs to replace them

#AI infrastructure#AI readiness#data governance...

Data Engineering

Your ‘Terabyte-Scale’ Data Pipeline Probably Processes 15 GB a Day

Data engineers on Reddit reveal the massive gap between resume inflation and reality, most incremental loads are 1-15 GB, not terabytes. Here’s what real-world volumes look like across industries and why domain matters more than raw size.

#Data Engineering#data pipelines#etl...

apache-hop

N8N’s RAM Addiction: Why Your ‘Free’ ETL Tool is Eating Your Server Alive

A French CTO’s search for lightweight data transformation tools reveals the brutal truth about open-source ETL: your ‘scalable’ solution might be the problem. Real benchmarks, real alternatives, real talk.

#apache-hop#etl#n8n

code-reuse

The Copy-Paste Tipping Point: When Duplication Beats Abstraction in Data Pipelines

The unspoken truth about Python packaging in data engineering: sometimes the ‘wrong’ choice is architecturally right. A field guide to knowing when to extract versus when to duplicate.

#code-reuse#dbt#python

agpl

MinIO’s Billion-Dollar Exit Strategy: How to Kill an Open Source Project in Six Easy Steps

An anatomy of trust destruction, how a unicorn startup systematically dismantled its community edition over 18 months, from license weaponization to binary removal, and what it means for the future of open core sustainability.

#agpl#minio#object-storage...

azure-data-lake

OneLake’s 30% Performance Tax: The Strategic Cost of Microsoft’s Unified Data Vision

Microsoft’s OneLake promises unified data access but delivers a 20-30% performance penalty for non-Fabric engines. This deep dive exposes the Redirect/Proxy API architecture creating a two-tier system that nudges enterprises toward ecosystem lock-in.

#azure-data-lake#cloud-lock-in#microsoft-fabric...

case-studies

Microservices Won’t Save You: The Hard Lessons from Shopify’s Modular Monolith and Discord’s ScyllaDB Migration

A deep dive into real-world system evolution, including Shopify’s journey with a modular monolith and Discord’s move from Cassandra to ScyllaDB, revealing practical trade-offs in scalability and maintainability.

#case-studies#database-migration#microservices...

data-ingestion

The AI Data Ingestion Gold Rush: Most Are Digging in the Wrong Place

Data engineers are throwing LLMs at ingestion pipelines, but production-grade wins are rare. Here’s where AI actually delivers value, and where it creates expensive new problems.

#data-ingestion#etl

data centers

AI’s Hidden Water Heist: Why the ‘It Just Evaporates’ Argument Is Draining Texas Dry

The viral Reddit debate about AI water consumption reveals a dangerous misunderstanding of how data centers extract value from local communities while hiding behind hydrological technicalities.

#data centers#environmental cost#sustainability...

AI engineering

AI Engineering Is Just Data Engineering in Disguise

AI engineering roles are largely rebranded data engineering jobs involving probabilistic data flows, prompt engineering as inputs, and embeddings as features. This perspective demystifies AI development and empowers data engineers to transition without panic or retraining.

#AI engineering#career transition#Data Engineering...

notebooks

Beyond #NotebookEverything: The Production Engineering Debt You Didn’t Know You Were Building

Jupyter notebooks revolutionized data exploration, but shipping them to production creates a silent reliability tax that compounds faster than your data grows. Here’s why the tools that accelerate development actively undermine operational excellence.

#notebooks#production-pipelines#reliability

duckdb

DuckDB in Production: The Embedded Database Challenging Enterprise Data Dogma

Real engineering teams are running DuckDB in production, cutting costs by 70% and outperforming Spark clusters. But is an embedded analytics database really ready for enterprise workloads, or are we just tired of overcomplicated data stacks?

#duckdb#motherduck#production