Ataccama ONE is a data-quality-led platform with an MDM module, a catalogue, lineage and observability, and the ONE AI Agent; it pushes profiling and quality rules down into Snowflake and Databricks.
Every Ataccama cell carries its source number; the sources are listed at the foot of the page.
| Question | Ataccama | Lima master data |
|---|---|---|
| Where the master data lives | The MDM hub keeps instance and master layers per entity in its own database, with history stored separately; quality and profiling can run inside Snowflake and Databricks; MDM matching still loads a copy [1][2]. | Golden records are governed views over the sources, read in place through the federated engine, and materialised only where latency needs it, in your lake, under the same keys. The transactions sit in the same bed, so a merged customer and its orders change together. |
| Reaching SAP, Oracle and the lakes | Cloud, on-premises and cloud-agnostic deployment (AWS, Azure, Snowflake); batch, streaming, messaging and API load interfaces [3][2]. | SAP S/4HANA and ECC through released read interfaces and change capture; Oracle Fusion through REST and BI Cloud Connector extracts, E-Business Suite through JDBC and open interfaces; Snowflake, Databricks and Fabric read in place; Salesforce, ServiceNow and Workday through their APIs. Write-back through BAPIs, IDocs, REST and web services, acknowledged and logged. |
| Golden record: matching and survivorship | Automated, manual and proposal-based matching ('most merges require approval'), rule-based plus AI Matching; a stable master id; golden, silver and instance records; reviewers report full reprocessing after rule changes on large volumes [4][5][6]. | Strong keys first (VAT ID, DUNS, GTIN, manufacturer part number), then standardised attribute and address matching, with a language model for the hard pairs; two thresholds per rule (review, merge); survivorship per field by source rank, recency, verification and steward override; unmerge restores the sources; every value keeps its source, rule and time. |
| Hierarchies and reference data | Hierarchies configured as 1:N or M:N relationships on instance or master layers, browsable in the web application; reference data management on the MDM page [7][3]. | Levels and several hierarchies per entity, kept as versions with validity dates and validated against the facts they aggregate; reference data and crosswalks kept as small domains with the same history, rules and security. |
| Rules, security and audit | '300+ built-in quality functions' with pushdown to SQL where supported; additive role-based access with column visibility lists and AND-combined row conditions checked on old and new values; an audit log of events and a history plugin with point-in-time queries, with only 'searchable' columns filterable [1][8][9]. | Column, row and business rules in one language, evaluated on save and on demand, including rules over aggregates and across tables; twelve security layers for read, write and delete; an append-only audit trail with point-in-time reads and logged reads of sensitive columns. |
| APIs and data services | REST (search, identify, history, overrides, tasks, streaming control) with Swagger, JSON only [10]. | REST, SOAP, SQL (JDBC and ODBC), events and an agent endpoint over one policy engine; the golden-record lookup answers with the record, the cross-reference and live related facts; page sizes and rate limits published per endpoint. |
| Analytics: pivots, measures, transactions | Quality monitoring and observability dashboards in ONE; pivots and measures over transactions are absent [2]. | Pivots on any hierarchy level with time on the columns and measures in the cells; calculated measures; data entry with disaggregation by weights, equal shares or a time profile; drill-down from a cell to the transactions and to the source row; charts beside grids; natural-language and SQL querying. |
| Pricing unit and trial | Named users, data objects and active quality configurations as the three dimensions, 'soft limits', prices unpublished; trials were withdrawn in 2025 in favour of a guided evaluation; Forrester TEI: 348% ROI [11][12][13]. | Platform capacity tiers (32C to 128C); domains, records, attributes and users included; the price is written before the work starts; a guided first domain on your data in one day; the showcase estate for demonstrations; a self-service sandbox tier is a decision pending (see the review notes). |
| Analyst placements 2025–26 | Gartner Magic Quadrant for MDM Solutions, April 6, 2026: Challenger, with 'native data quality capabilities' as the noted strength; Leader in the 2026 Gartner MQ for Augmented Data Quality (as cited by the vendor) [14][3]. | Analyst evaluation is the purpose of this page set; placements to date: none. The platform pages are organised on Gartner's eight mandatory features and four implementation styles so a briefing lands in the right market. |
Choose Ataccama when data quality, profiling and observability across the lakehouse are the first priority and MDM is one module among them, and when pushdown quality on Snowflake or Databricks is the architecture. Its quality functions and its pushdown are real advantages for a lakehouse-first data team [14].
Lima suits the enterprise whose master data should be governed on the rows in place across SAP, Oracle, the CRM and the lakes, with the transactions in the same bed: the golden record and its orders in one query, rules over totals, pivots on golden dimensions with data entry and disaggregation, drill-downs to the source row, and a headless lookup that answers with live related facts. Twelve security layers and an append-only audit trail apply to every door, and the price is written before the work starts.
The domain catalogue shows thirty domains with, for each, the objects read in place in SAP S/4HANA, Oracle Fusion, Salesforce, Workday, ServiceNow, Snowflake and Databricks and the interface used for the write-back.
Facts change; re-check the sources at each review and re-date the page.
Bring two systems that hold the same customers, suppliers or materials. In one working day we connect them, run the match on live rows, review the queue with your steward and publish the first golden view with its API.
