Know which data you can trust

Four ways to use it

• CLI

  A single native command-line program that profiles and checks files on your own machine with no server and nothing uploaded. It also works as a client for the hosted API.

  dqaas run customers.csv -o report.html

• API

  REST with a generated OpenAPI document: register sources, run jobs, manage rules and read the catalogue and reports.

  GET /v1/datasets/{id}/report?format=md

• MCP

  Give an AI assistant the same access: a local MCP server that can also profile a file on your machine, and a hosted MCP endpoint that uses your access token.

  POST /mcp  (Authorization: Bearer dqat_...)

• In the browser

  Attach a file and profile it on your own device. Nothing is uploaded.

  Open Attach locally in the console

All four share the same checks and report format. Browser results are computed on the device.

How it works

1. 1

   Connect or upload

   Upload a file or register a URL or table in a project.

2. 2

   Profile and check

   DQaaS profiles every column, suggests rules, runs them and scores the dataset.

3. 3

   Catalogue and report

   Add owners, descriptions and classifications, track lineage and export a report.

export DQAAS_URL=https://your-dqaas-host
dqaas login --token dqat_...
dqaas upload orders.csv --project PROJECT_ID --wait
dqaas analyze DATASET_ID --wait
dqaas rules DATASET_ID --suggest
dqaas report DATASET_ID --format html -o report.html

POST /v1/datasets/upload
POST /v1/datasets/{id}/analyze
GET  /v1/datasets/{id}/rules/suggestions
GET  /v1/datasets/{id}/report?format=md
GET  /v1/catalogue/search?q=customer
Authorization: Bearer dqat_...

Capabilities

• Ingest files and sources

  Included

  Start from what you already have.

  Upload CSV, TSV, JSON, JSONL, Parquet, Avro, Arrow, XLSX or embedded database files, or register an HTTPS URL or a read-only Postgres table. The format is detected from content and name.

• Profiling

  Included

  See what is really in a column.

  Types, null and distinct counts, value statistics, placeholder detection, and schema and statistical drift between profiles.

• Quality rules and scoring

  Included

  One score per dataset, with the failing rows counted.

  Suggested rules from the profile, rule packs and your own checks. Each rule reports failed over total rows; scores are weighted by severity and grouped by quality dimension.

• Sensitive-column detection

  Included

  Find likely personal data early.

  Name and pattern heuristics flag columns such as email, phone or date of birth. Findings are labelled inferred, and sensitive sample values are redacted.

• Profile on your own device

  Included

  Check a file without sending it anywhere.

  Attach a CSV, TSV, JSON, JSONL or Parquet file and profile it on your own device; the file never leaves it. Optionally publish only the profile and rule results to the catalogue. Browser results are computed locally and are not verified by the server.

• Catalogue and governance

  Included

  Everyone finds the right table and who owns it.

  Full-text search, glossary, column descriptions and classifications, policies and violations, stewardship tasks and coverage indicators.

• Lineage and impact analysis

  Included

  Know what breaks downstream.

  Lineage edges are inferred or verified (verified requires evidence). Impact analysis runs upstream or downstream, optionally on verified edges only.

• Reports

  Included

  Share results with people who never log in.

  Dataset and project reports as JSON, Markdown, HTML or PDF.

• REST API, OpenAPI, CLI and MCP

  Included

  Fit it into pipelines and assistants.

  A bearer-token REST API with a generated OpenAPI 3.1 document, a command-line client, and an MCP server with 16 tools.

• Roles, tokens and audit log

  Included

  Control and trace access.

  Viewer, editor, admin and owner roles, scoped and revocable access tokens, and a hash-chained audit log with a verify endpoint.

• Quotas and usage metering

  Included

  See what you use.

  Monthly quotas with usage shown in the console and API.

• Interval schedules

  Included

  Re-check data on a rhythm.

  Recurring jobs on a fixed interval from 5 minutes to 1 year.

Who it is for

• Developers and data engineers

  Add checks to pipelines through the CLI or REST API.

• Analysts

  See which tables are trustworthy before building on them.

• Data teams and stewards

  Track ownership, classification and open stewardship tasks.

• AI-assistant users

  Query quality and catalogue data through MCP.

Data handling principles

• Source rows are transient: they are processed for the job and deleted at its end. Uploads are kept only if you ask.
• Metadata is persisted: profiles, rule results, scores, catalogue entries and an audit log.
• Sensitive values are redacted: sensitive columns store no top values, no min/max and no failing examples.
• Strict privacy mode stores no sample values at all.
• Remote sources are checked against SSRF: only HTTPS, no embedded credentials, and hosts resolving to private or loopback addresses are blocked on every redirect.
• Findings are labelled inferred or verified. Sensitive-column detection and relationships are inferred; lineage is verified only with evidence.
• No accuracy claim without reference data: DQaaS reports completeness, validity, uniqueness and similar checks, not accuracy.
• In-browser mode: files are profiled on your own device and never leave it.

See the security page for controls and their limits.

Pricing

Roadmap

• Roadmap

  Payment processor integration

• Roadmap

  Single sign-on beyond GitHub

• Roadmap

  Cron-expression schedules

• Roadmap

  Column-level lineage extraction from SQL

• Roadmap

  OpenLineage and ODCS import and export

Frequently asked questions