Data quality,
without the DQ tax.
Know the integrity of your data at the partition, not the incident. A 0–100 Data Integrity Score, your own rules, millions of rows — without the bloated DQ platform.
You don't need a bloated DQ platform
Legacy data quality tools charge enterprise money to run some SQL. We give you the rules, the scale, and the score — and you keep the keys to your data.
Everything you need to trust every row
DIS runs inside your warehouse across every partition — declaring rules, scoring results, and surfacing issues before they reach dashboards, models, or ad platforms.
Checks run every 30 minutes on the partition that just landed. Bad rows are caught before they flow to dashboards, ad platforms, or ML features.
30-min bucketsAll 9 built-in rule types — Not Null, Unique, Freshness, In Range, In List, Regex, Row Count Min, Completeness, Conditional Not Null — plus custom SQL for the rules you uniquely need.
9 types + custom SQLEvery table, every column, every partition gets a single composite score. Scan once, triage anywhere — no more hunting through raw pass/fail counts.
0–100 · weightedTrino for OLAP/Iceberg targets, direct SQL for OLTP databases. No data egress, no replication, no shadow warehouse of duplicate rows.
OLAP · OLTPRules, targets, and schedules live as config rows in your transactional DB. No sidecar agent, no separate SaaS, no new thing for your SRE team to babysit.
Config-drivenSee pass rate per target-rule combination across every dataset — the squares tell you what's green, what's amber, and what you haven't covered yet.
Target × RuleData quality coverage at a glance
One grid — targets on the rows, rule types on the columns, pass rate in the cells. You see what's green, what's failing, and what you haven't covered yet, without digging through a single dashboard.
9 rule types. Declarative. Your SQL when you need it.
Every rule is a config row in your transactional DB — versioned, reviewable, and portable. Attach rules to columns, schedule once, and let the engine decide where to run them.
Flag the partition when any value in the column is NULL. The simplest integrity guard — at warehouse scale.
Every value in the partition must be unique. Catches duplicate order IDs and transaction leaks as they land.
Percentage of non-null values must stay above a configurable threshold. Partial coverage catches drift early.
Numeric values must sit within a min/max bound. Catches negative totals, impossible ages, outlier prices.
Values must match a configured allow-list. Great for currencies, country codes, event names, or status fields.
Values must conform to a regex pattern. Enforce SKU formats, ISO country codes, UUID shapes, or tracking IDs.
Table or partition must contain at least N rows. Surfaces pipelines that silently stopped or underdelivered.
The most recent row must have landed within the last N hours. Catches stalled upstream jobs and delayed ingest.
When one column equals a value, another column must not be NULL. For fields required only in specific contexts.
CUSTOM_SQL · `SELECT COUNT(*) FROM ...`
From rule to score in minutes
Write rules as config rows — 9 built-in types or your own custom SQL. Versioned in the repo alongside the rest of your data stack.
Point each rule at a specific table × column × partition strategy. One rule can cover many datasets, and the mapping is fully reusable.
Batch jobs run every 30 minutes on partitioned buckets, pushing checks as native SQL to the engine where your data already sits.
Results land in auditable Iceberg tables. The Data Integrity Score recomputes, the Health Map updates, alerts route to Slack or PagerDuty.
Connects to the stack you already run
20+data sources · alert destinations · SSO
Ready to trust every row?
Turn on Data Integrity Score and catch issues at the partition, not the incident — in the warehouse you already run.