Engineering und Produkt
Was wir lernen, während wir den Daten-Stack bauen, den wir selbst gern gehabt hätten.
Technische Entscheidungen. Messungen. Sicherheitstests. Dinge, die kaputtgingen. Dinge, die wir falsch gemacht haben. Und die Überlegungen hinter dem Produkt.
Schwerpunkte
01
Analytics aufbauen, ohne eine Datenabteilung aufzubauen
Warum Daten zu sammeln leicht ist, sie nutzbar zu machen aber nicht.
02
Datenfragen in normaler Sprache stellen
Wie Analytics in natürlicher Sprache funktionieren kann, ohne einer KI uneingeschränkten Zugriff auf Ihr Warehouse zu geben.
03
Die Infrastruktur hinter Analytics für Kunden
Die Probleme bei Datenaufnahme, Speicherung, Isolation und Abfragen, die sich hinter „Gebt den Kunden einfach ein Dashboard“ verbergen.
04
Was die Messungen sagen
Performance-Experimente, Benchmarks und die Grenzen rund um die Zahlen.
05
Sicherheit, die Sie prüfen können
Red-Team-Ergebnisse, Mandantenisolation und die Kontrollen, die wir tatsächlich haben.
Keine Blackbox. Keine perfekte Geschichte.
Wir veröffentlichen Entscheidungen, Messungen und Fehler, weil das Produkt verständlich sein sollte, bevor man ihm vertraut.
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Put your dashboards on your own page
A read-only public API for the dashboards you share, an open source library that draws them on any web page, and short-lived sessions so that no credential of ours ever sits in your HTML. Here is how it works, and the decisions that made it safe to hand to a stranger.
engineeringapisecurity -
We pointed Event Horizon at Event Horizon
Our own servers, containers, ClickHouse and API requests now flow through the same pipelines, builder, anomaly bands and forecasts our customers use. Here is what that looks like, and how to build the same thing for your stack.
engineeringobservability -
The price that changed twice
Most ecommerce data lives in MySQL or Postgres, and a row doesn't always mean an event. Here's what we built so a changing product still has one current answer, and a real bug we found chasing the fix.
engineeringecommerce -
The chart that took thirty seconds to draw
A zoom fix we'd shipped days earlier turned out to be incomplete, and the bug we found chasing it down had nothing to do with what we blamed first. Here's the mistake, the fix, and a rule we shipped and un-shipped the same day.
engineeringperformance -
Compare any period to the one before
Month over month, quarter over quarter, year over year. The most common question about any number is "compared to what?", and it should not need SQL or a calculator. Here is where it lives in the builder and how to read it.
product -
Ask your data a question, in your own words
Type the question the way you'd say it out loud, and get a query matched to the values that are really in your data. On our own machines, and only ever showing you what it can prove.
productai -
A blank canvas is a question
Our ingestor opened on an empty grid, which felt like freedom and was actually an interrogation. Fixing it meant admitting that every question we asked first was about our architecture, and none of them were about the customer.
engineeringproduct -
The reasons I gave myself were wrong
I spent years being sure about how analytics should scale. A database in a cluster, OLAP cubes, then a columnar engine. Every one of those certainties died against a measurement, including the ones I used to justify the migration I still think was right.
engineeringarchitecture -
SQL and notebooks, without a warehouse to run
The default stays no-SQL, for the people who have the question but not the query. But some of your team writes SQL, and lives in a notebook. So we added both, on rails: you write against your own tables, never our warehouse, and every query is cost-checked before it runs.
productsqlanalytics -
Security you can check, not just trust
Multi-tenant isolation is easy to claim and hard to prove. So we red-team our own, against the live system, and publish the result. Here's how your data stays yours, and where to check us.
securitytrust -
Turning events into decisions
Every team collects event data. Far fewer turn it into answers. Here's the gap we set out to close, and why we built the whole stack, not another dashboard.
analyticsproduct