Top all-time · #9712
Monte Carlo
Company blog · montecarlodata.comdatabusiness
Monte Carlo is the company blog at montecarlodata.com, writing about data and business. Since 2020, 8 different Hacker News readers have linked it 21 times, most often “The Chaos Data Engineering Manifesto: Spare the Rod, Spoil Prod”. It is still writing: the last post was on 2026-10-30, and you can follow it by feed.
Too few sampled comments (2) to say how often HN recommends it.?
Why HN links it
Commenters link Monte Carlo's site for its data observability product, which covers data lineage, data catalogs and anomaly detection, and for a company hiring post. The comments do not assess any writing.
Summarised by Claude from a sample of 2 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
Go beyond data quality to unlock true AI observability with the only end-to-end data and AI observability platform for enterprise teams.
Most-linked posts?
- The Chaos Data Engineering Manifesto: Spare the Rod, Spoil Prod2 people · 2 mentions · first linked 2023-02-23
What Hacker News says?
Monte Carlo | Data Science; Data, Backend, & Frontend Engineers | Remote | Full-Time | https://www.montecarlodata.com/ Monte Carlo helps data teams manage data observability in the same way that New Relic helps engineering teams manage application observability. Monte Carlo helps solve one of the biggest challenges enterprises face today: making sure data can be trusted at all times by minimizing data downtime. Mont…
Comment linking /
on “Ask HN: Who is hiring? (April 2021)”Submitted Data Catalogs Are Dead; Long Live Data Discovery as a story · 30 points
HN discussionSeems like the the key pillars are: freshness, volume, schema, distribution, and lineage. Makes more sense this way, I think... If you think about metrics, traces, and logs (software observability pillars) as three distinct things, it's hard to view metadata separate from metadata, lineage, or logs. Metadata is kind of the glue that holds everything together. This article has more relevant sources, IMO, even if it i…
Comment linking /blog-what-is-data-observability/
on “The four pillars of data observability: metrics, metadata, lineage, and logs”Submitted Data Engineers Spend 2 Days per Week Firefighting Bad Data as a story · 16 points
HN discussionHave you heard of Monte Carlo? Data lineage + data catalog + data anomaly detection: https://www.montecarlodata.com/product/
Comment linking /product/
on “Data Catalogs Are Dead; Long Live Data Discovery”Submitted Why Production Machine Learning Fails – and How to Fix It as a story · 7 points
HN discussionSubmitted ELT Schedules Can Improve Root Cause Analysis for Data Engineers as a story · 5 points
HN discussionSubmitted How to extract data observability metrics from Snowflake as a story · 5 points
HN discussionSubmitted The Non-Engineer’s Guide to Bad Data as a story · 3 points
HN discussionSubmitted The Quick and Dirty Guide to Building Your Data Platform as a story · 3 points
HN discussionSubmitted Vimeo Achieved E2E Visibility in Snowflake and Looker with Monte Carlo as a story · 2 points
HN discussionSubmitted Anomaly Detection: Why Your Data Team Is Just Not That into It as a story · 2 points
HN discussionSubmitted Decoding the Data Mesh as a story · 2 points
HN discussionSubmitted Using LLMs to Score Customer Conversations as a story · 1 points
HN discussionSubmitted The Chaos Data Engineering Manifesto: Spare the Rod, Spoil Prod as a story · 1 points
HN discussionSubmitted The Chaos Data Engineering Manifesto: Spare the Rod, Spoil Prod as a story · 1 points
HN discussionSubmitted Monte Carlo Raises $135M Series D as a story · 1 points
HN discussionSubmitted First small batch data observability platform as a story · 1 points
HN discussionSubmitted Data Software-as-a-Service: The Case for a Hybrid Deployment Architecture as a story · 1 points
HN discussionSubmitted Monte Carlo raises $16M to build Data Reliability platform as a story · 1 points
HN discussionSubmitted Good Tales of Bad Data as a story · 1 points
HN discussion