Top all-time · #14913
Metriql Docs Blog
Company blog · metriql.com/blogdataprogramming
Metriql Docs Blog is the company blog at metriql.com, writing about data and programming. Since 2021, 5 different Hacker News readers have linked it 11 times. The last post was in 2021, but the archive is still widely read.
Too few sampled comments (3) to say how often HN recommends it.?
Why HN links it
HN readers link the MetriQL docs blog, which covers an open-source metrics layer that works with dbt. Comparisons with Looker and Lightdash appear, but the comments only mention the posts in passing.
Summarised by Claude from a sample of 3 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
Headless BI - model your data once and consume it wherever you want
Most-linked posts?
- /2 people · 4 mentions · first linked 2021-08-25
What Hacker News says?
Presto is pretty successful but its focus is to be distributed query engine, not a proxy layer for the existing query engines. We use Trino ( formerly Presto) as our query layer and do something similar to Hydra at Metriql [1] with a fairly different use-case. Data people provide a semantic layer with the mecrics and expose them to 18+ downstream tools. [1]: https://metriql.com
Comment linking /
on “Launch HN: Hydra (YC W22) – Query any database via Postgres”Submitted Show HN: Metriql – The first open-source headless BI solution as a story · 10 points
HN discussionI believe that we’re working on a similar product that you described. Our metrics layer, metriql, extends dbt for metrics definitions and provides an open-source CLI that serves your metrics to the data tools. We have REST API and use Trino protocol so in case you support Trino (formerly Presto) you already support metriql. :) Here is the link: https://metriql.com
Comment linking /
on “Launch HN: Evidence (YC S21) – Web framework for data analysts”Submitted OLAP in the Modern Data Stack as a story · 5 points
HN discussionWe're using Superset to enable our analysts to explore our clients' SEM/SEO/analytics data. It also posts alerts to Slack when, say, the daily session count of a website isn't what was expected given the historical data. Yeah, it's a little rough to get going, but once it is, we've found it to be a really powerful (and actively developed!) BI tool. It's even better with dbt + MetriQL [0], which can automatically syn…
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on “Open source Business intelligence platform made with Python”Submitted How we built the Metriql public demo as a story · 3 points
HN discussionCompare https://metriql.com/introduction/creating-datasets and https://docs.lightdash.com/guides/how-to-create-metrics I though you are affiliated somehow, but looking at it now, it seems you just use the same documentation website generator :)
Comment linking /introduction/creating-datasets
on “The Rise and Fall of the OLAP Cube (2020)”Submitted Best Practices to optimize query costs in BigQuery as a story · 1 points
HN discussionWhile native materialized view feature is a great start, unfortunately they're not useful in a practical way if you have data lineage. They works like a black-box and they can't guarantee the query performance. The new generation ELT tools such as dbt partially solve this problem. You can model your data and incrementally update the tables that can be used in your BI tools. Looker's Aggregate Awareness is also a gre…
Comment linking /introduction/aggregates
on “The Rise and Fall of the OLAP Cube (2020)”The main problem is that it's not easy to drill down into a metric in most BI tools because the connection between the dashboard and the source data is usually missing. Looker is one of the first companies that target this specific issue; you transform, model the data, define your metrics before creating your first dashboard. It takes too much effort (also money) to create a (basic?) dashboard because it's not just…
Comment linking /
on “Notes on the Perfidy of Dashboards”As a current Looker power-user, this looks really solid. One thing I’m not sure about though: can you use the metrics outside of the native tool, and if so how? That is, I see Looker as a BI tool, not a metrics layer, since you mainly use the metrics you define inside Looker, not in other tools. On the other hand, something like MetriQL[0] is a pure metrics layer that can supposedly be used anywhere. Is this both? I…
Comment linking /
on “Show HN: Low-Code Metrics Store”