Data Quality Tools

Ataccama ONE vs DataMatch Enterprise

Compare Ataccama ONE and DataMatch Enterprise side-by-side. See feature differences and pricing in the Data Quality Tools category.

Ataccama ONE

by Ataccama

No reviews yet

Pricing: Quote only, scoped by named users, data objects and active quality checks. View-only users are unlimited, limits are soft, extra AI queries on request. Demo or guided evaluation instead of a trial.

Ataccama ONE is a data quality platform for large enterprises that joins rules, profiling, catalog, lineage, observability and master data, sold by quote.

12 /17 features Full profile

DataMatch Enterprise

by Data Ladder

No reviews yet

Pricing: Quote only for every edition: DataMatch Enterprise, + API, and + Address Verification (a premium add-on). Trial download of the base edition; demo for the others.

DataMatch Enterprise is Data Ladder's data matching software for deduplicating and cleansing records across files and databases without code. Quote only.

7 /17 features Full profile

Feature Comparison

Data Quality Tools features side-by-side

Feature	Ataccama ONE 12/17 features	DataMatch Enterprise 7/17 features
Automated column profiling Distributions, null rates, cardinality and patterns are computed for every column on demand.	Yes	Yes
Declarative quality rules in YAML or SQL Checks are written as configuration or SQL and stored in version control.	No	No
No-code rule authoring Business users create checks from a form or template without writing code.	Yes	Yes
Reusable rule library across datasets A central library of rules is applied to many tables and systems from one definition.	Yes	No
Anomaly detection on quality metrics Row counts, distributions and rule pass rates are baselined and deviations are flagged.	Yes	No
Quality score per dataset Each table carries a computed score or trust index from its rule results.	Yes	No
Quality checks in CI and orchestration runs Checks execute inside pipelines or pull requests and can block a deployment.	Yes	No
Data contracts with enforcement Producers and consumers agree on schema and quality expectations that are checked automatically.	No	No
Standardisation and cleansing transforms Fix formats such as phone numbers, country codes and casing in place.	Yes	Yes
Duplicate detection with fuzzy matching Near-duplicate records are found with configurable similarity rules.	Yes	Yes
Address and reference data validation Postal addresses and coded values are checked against reference data.	No	Yes
Issue assignment and remediation workflow Failed checks become issues with an owner, status and resolution history.	Yes	No
Alerts to chat and ticketing tools Rule failures post to Slack or Teams and can open tickets in the team's tracker.	Yes	No
Quality results published to a catalog Check results and scores are pushed to a data catalog so consumers see them on the asset.	Yes	No
Checks across warehouses, lakes and files The same rules run against cloud warehouses, lake tables, databases and flat files.	Yes	Yes
Open source checks library The checks engine is open source and can run without the hosted product.	No	No
Private deployment in your cloud The platform can run inside your own cloud account rather than as multi-tenant SaaS.	No	Yes
Overall Rating	No reviews yet	No reviews yet

Ataccama ONE 12/17 features

Automated column profiling: Yes
Declarative quality rules in YAML or SQL: No
No-code rule authoring: Yes
Reusable rule library across datasets: Yes
Anomaly detection on quality metrics: Yes
Quality score per dataset: Yes
Quality checks in CI and orchestration runs: Yes
Data contracts with enforcement: No
Standardisation and cleansing transforms: Yes
Duplicate detection with fuzzy matching: Yes
Address and reference data validation: No
Issue assignment and remediation workflow: Yes
Alerts to chat and ticketing tools: Yes
Quality results published to a catalog: Yes
Checks across warehouses, lakes and files: Yes
Open source checks library: No
Private deployment in your cloud: No

No reviews yet

DataMatch Enterprise 7/17 features

Automated column profiling: Yes
Declarative quality rules in YAML or SQL: No
No-code rule authoring: Yes
Reusable rule library across datasets: No
Anomaly detection on quality metrics: No
Quality score per dataset: No
Quality checks in CI and orchestration runs: No
Data contracts with enforcement: No
Standardisation and cleansing transforms: Yes
Duplicate detection with fuzzy matching: Yes
Address and reference data validation: Yes
Issue assignment and remediation workflow: No
Alerts to chat and ticketing tools: No
Quality results published to a catalog: No
Checks across warehouses, lakes and files: Yes
Open source checks library: No
Private deployment in your cloud: Yes

No reviews yet

Pricing

Ataccama ONE

Quote only, scoped by named users, data objects and active quality checks. View-only users are unlimited, limits are soft, extra AI queries on request. Demo or guided evaluation instead of a trial.

See all plans

DataMatch Enterprise

Quote only for every edition: DataMatch Enterprise, + API, and + Address Verification (a premium add-on). Trial download of the base edition; demo for the others.

See all plans

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