Reltio is a cloud-native multidomain MDM built on a graph of entities, relationships and interactions, sold as the Reltio Data Cloud with the AgentFlow agents.
Every Reltio cell carries its source number; the sources are listed at the foot of the page.
| Question | Reltio | Lima master data |
|---|---|---|
| Where the master data lives | Entities, relationships and 'interactions' (transactions such as purchases or monthly sales figures) are stored in the Reltio tenant; data pipelines copy them to Snowflake and BigQuery near real time for analytics [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 | Integration through '1,000+ prebuilt connectors', an API-first platform and event streaming; the tenant is SaaS on AWS, Google Cloud and Azure [3]. Reltio is to become 'a core capability within SAP Business Data Cloud' [4]. | 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 | Real-time cleanse, match and merge; match groups typed automatic, suspect and relevance-based; survivorship strategies including last-updated, source system, frequency, aggregation, oldest, min/max and pinned values; the ML matcher Match IQ was deprecated on February 16, 2026 in favour of rules and the LLM-augmented FERN [5][6][7]. | 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 | Graph types define hierarchies; an entity can belong to several graphs, so alternate hierarchies work by design, with multiple parents flagged in the UI; reference data management standardises codes and hierarchies [8][9]. | 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 | Metadata security at tenant, type and attribute level with row filters per role and masked reads for interaction attributes; the entity-history API returns the 1,000 most recent events per entity, with an activity-log API for tenant-wide search [10][11]. | 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 entities, crosswalks, merge and unmerge, potential-matches and a 'search before create' conditional endpoint; an export service; a Reltio MCP Server [3][12]. | 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 | Interactions can hold monthly figures next to profiles; pivot and measure views are absent from the Hub, and the RIA assistant answers questions and drafts visualisations; analysis runs in the warehouse copy [2][13]. | 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 | Pricing is unpublished on the site; the licensing unit in the documentation is 'consolidated profiles'; the AWS Marketplace lists Reltio Data Cloud at $15,600 per month per 'Unit'; evaluation needs signed terms that bar production and personal data [14][15][16]. | 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: Leader, 'positioned furthest for Completeness of Vision'; Forrester Wave MDM Q2 2025: Leader and 'Customer Favorite' [17][18]. | 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 Reltio when a real-time customer or life-sciences profile in a SaaS tenant is the goal, when the relationship graph and the near-real-time warehouse pipeline fit the architecture, and when an SAP-owned roadmap is welcome. Its FY26 scale ($185M ARR, 200+ enterprises) and the graph model are real advantages for a large B2C or life-sciences programme [19].
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.
