DQaaS

Know which data you can trust

DQaaS is a data quality service. Developers, analysts and data teams use DQaaS to profile datasets, check them against rules, score their quality and keep a governed catalogue, from a web console, a REST API, a CLI or an AI assistant.

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Need an invite? Request an invite or write to [email protected]

Four ways to use it

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

How it works

  1. Connect or upload

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

  2. Profile and check

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

  3. Catalogue and report

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

CLI

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

REST API

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

Who it is for

Data handling principles

See the security page for controls and their limits.

Pricing

Free during launch. All accounts are free, with these limits per month:

  • 1 GB of data scanned
  • 10 minutes of compute
  • 2 scheduled jobs
  • 50 MB of stored metadata

Pricing will be announced before any charge.

Build status

As of 2026-10-31. We list what is released, what is still being built and what has not been confirmed outside our own tests.

Roadmap

Frequently asked questions

What is DQaaS?

DQaaS is a data quality service. It profiles datasets, checks them against rules, scores their quality and keeps a governed catalogue with lineage, reports and an audit log. You can use it from a web console, a REST API, a command-line program, an MCP server for AI assistants, or in the browser on your own device.

Can I try it without an account or a server?

Yes. Install the command-line program and run it on a file: it profiles and checks the file on your own machine, writes a report as Markdown, HTML, PDF or JSON, and uploads nothing. Run dqaas run customers.csv -o report.html.

Which platforms does the command-line program support?

Linux x86_64, Linux arm64 and macOS on Apple silicon. A Windows build does not exist yet.

Does it work with Prometheus, OpenLineage and data contracts?

Yes, within limits. It serves per-dataset quality gauges to Prometheus, exports and ingests OpenLineage events, and imports and exports the rule parts of Open Data Contract Standard (ODCS v3.2.0) contracts. Anything that cannot be mapped is listed, not guessed.

Does DQaaS store my data?

Not your rows, unless you ask. On upload, source rows are processed for the job and deleted when it ends; an upload is kept only if you choose to retain it. Metadata is persisted: profiles, rule results, scores, catalogue entries and audit records. Sensitive values are redacted, and strict privacy mode stores no sample values at all.

Can I check a file without uploading it?

Yes. Attach a CSV, TSV, JSON, JSONL, Parquet, Avro, XLSX, Arrow or embedded database file and it is profiled on your own device; the file never leaves it. Those results are computed locally and are not verified by the server. You can publish just the profile and rule results to the catalogue.

Which formats are supported?

Upload: CSV, TSV, JSON, JSONL, Parquet, Avro, Arrow, XLSX and embedded database files, plus HTTPS URLs and read-only Postgres tables. On your own device: CSV, TSV, JSON, JSONL, Parquet, Avro, XLSX, Arrow and embedded database files (first table).

How is quality scored?

Each rule counts failed rows against total rows, giving a score from 0 to 1 that passes at its threshold. Rule scores are weighted by severity and grouped by quality dimension into an overall dataset score.

Is accuracy measured?

No. Accuracy needs trusted reference data, which DQaaS does not have. It reports completeness, validity, uniqueness and similar checks, and makes no accuracy claim without reference data.

How reliable is sensitive-data detection?

It is heuristic, based on column names and value patterns, so it can miss or misflag columns. Findings are labelled inferred. Use strict privacy mode if a miss is unacceptable.

What does it cost?

Nothing during launch. All accounts are free with monthly limits (1 GB scanned, 10 minutes of compute, 2 scheduled jobs, 50 MB of stored metadata). Pricing will be announced before any charge.

How do I sign in?

Sign in with GitHub on the sign-in page. Access can also be by an access token for automation.

How do I connect an AI assistant?

The CLI includes a local MCP server with 50 tools, and a hosted MCP endpoint exposes 49 of them with your token or OAuth sign-in. Tools cover dataset listing, profiling, rule suggestion and acceptance, drift, reports, contracts, glossary, stewardship tasks, schedules, lineage and impact analysis.

Is DQaaS certified or compliant with a standard?

No certification or compliance attestation is claimed. See the security page for the controls that exist and their limits.