Hacker News with Generative AI: Data Engineering

Logs Don't Lie: Debugging My Data Engineering Crisis in 2025 (datobra.com)
Every data pipeline has its breaking point. Mine came in late 2024, throwing errors I couldn’t ignore. Logs showed signs of stagnation, over-processing, and the need for a refreshed perspective.
Should you ditch Spark for DuckDB or Polars? (milescole.dev)
There’s been a lot of excitement lately about single-machine compute engines like DuckDB and Polars. With the recent release of pure Python Notebooks in Microsoft Fabric, the excitement about these lightweight native engines has risen to a new high. Out with Spark and in with the new and cool animal-themed engines— is it time to finally migrate your small and medium workloads off of Spark?
Data-engineer-handbook: everything to learn about data engineering (kifinity.com)
We need data engineering benchmarks for LLMs (structuredlabs.substack.com)
Tools like Copilot and GPT-based copilots promise to reduce the repetitive burden of data engineering tasks, suggest code, and even debug complex pipelines. But how do we measure whether they’re actually good at this? Frankly, the industry is lagging behind when it comes to evaluation methods. While SWE-bench offers a framework for software engineering, data engineering is just left out—no tailored benchmarks, no precise way to gauge their effectiveness. It’s time to change that.
Dismantling ELT: The Case for Graphs, Not Silos (jack-vanlightly.com)
ELT is a bridge between silos. A world without silos is a graph.
The Data Engineering Handbook (github.com/DataExpert-io)
This repo has all the resources you need to become an amazing data engineer!
Understanding privacy risk with k-anonymity and l-diversity (marcusolsson.dev)
Imagine you’re a data analyst at a global company who’s been asked to provide employee statistics for a survey on remote working and distributed teams. You’ve extracted the relevant employee data, but sharing it as-is could violate privacy laws. How can you anonymize this data while ensuring it’s still useful? In this article, you’ll learn about k-anonymity and l-diversity—two valuable techniques in privacy engineering to help you reduce the privacy risk in datasets.
Ask HN: How to learn UI/UX as a data/BE engineer? (ycombinator.com)
Hi HN,<p>coming from a data/ BE background I feel extremely familiar with reasoning about systems and performance from the cloud-infra to the pipeline stack level.
Dbt – Incremental but Incomplete (tobikodata.com)
Earlier this month, dbtTM launched microbatch incremental models in version 1.9, a highly requested feature since the experimental insert_by_period was introduced back in 2018. While it's certainly a step in the right direction, it has been a long time coming.
I spent 5 hours learning how ClickHouse built their internal data warehouse (vutr.substack.com)
My name is Vu Trinh, and I am a data engineer.
Data Engineering Vault: A 1000 Node Second Brain for DE Knowledge (ssp.sh)
Welcome to the Data Engineering Vault an integral part of my Second Brain. It’s a curated network of data engineering knowledge, designed to facilitate exploration and discovery. Here, you’ll find over 100+ interconnected terms, each serving as a gateway to deeper insights.
Rɐbbit Dynamic Datascapes (github.com/ryrobes)
As a long-time dashboard builder, data engineer, and UI hacker - I've always wanted something in-between Tableau & building bespoke web data products to ship answers to my users. The tools were too rigid at times, and building everything from scratch can be tiresome. The eternal push/pull of DE and SWE approaches, as many who work in BI can attest to. How could I have the flexibility & re-usability of code, but the compositional freedom & direct manipulation of a builder tool?
How to Build a Scalable Ingestion Pipeline for Enterprise GenAI Applications (enterprisebot.ai)
Timeseries Indexing at Scale (krylysov.com)
Exploiting column chunks for faster ingestion and lower memory use (rerun.io)
Exploiting column chunks for faster ingestion and lower memory use (rerun.io)
New Apache Airflow Operators for Google Generative AI (cloud.google.com)
Lessons Learned from Scaling to Multi-Terabyte Datasets (v2thegreat.com)
Open Source Python ETL (amphi.ai)
Show HN: Pathway – Build Mission Critical ETL and RAG in Python (NATO, F1 Used) (github.com/pathwaycom)
Using Parquet's Bloom Filters (influxdata.com)