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The comparison at a glance/AnalyticsCreator

Yres or AnalyticsCreator? The differences at a glance

Yres and AnalyticsCreator both automate a data warehouse on Azure SQL, but differently. AnalyticsCreator is a design tool: it generates the data model in Kimball, Data Vault 2.0, Inmon or 3NF plus a semantic model for Power BI, Tableau and Qlik, and the generated code is yours. Yres deliberately generates no model; it delivers the loading layer with full history, per-change deployment, health checks, test suite, scaling and archiving, without a server of its own. Yres has its own connectors for AFAS and Exact Online and costs €350 to €997 per month; AnalyticsCreator starts at €800 per month per user.

Discuss your situation with a data architect

Last checked: 2026-09-22

The differences that matter most

  • AnalyticsCreator generates the data model in Kimball, Data Vault 2.0, Inmon or 3NF and a semantic model for Power BI, Tableau and Qlik; Yres deliberately does not and delivers the history layer on which you build any method yourself.
  • Yres has its own connectors for AFAS and Exact Online and is an official partner of both; for AnalyticsCreator we found neither mentioned on the website or in the documentation, only generic access through CData, a third party.
  • AnalyticsCreator offers unlimited environments at no extra cost, with a backup beforehand and drift blocking; Yres has one environment in Essentials, two in Advanced and six in Ultimate, with impact analysis and reviewing the SQL beforehand.
  • Yres runs without a server of its own in your own Azure and keeps metadata in your tenant; AnalyticsCreator requires a Windows desktop client and, per its own Trust page, sends metadata to the generation engine in Germany, which per the same page makes it unusable for air-gapped environments.
  • Yres ships operations in the product, with about 80 health checks, a test suite of ±1,655 checks, database scaling from Advanced up and archiving to Parquet; AnalyticsCreator is a design tool without a runtime of its own, and these items are not mentioned in its documentation.

Everything side by side

The same rows as on the full comparison, now only Yres and AnalyticsCreator.

✅ yes · ◐ partly or through a workaround · ❌ no · – not publicly documented

YresAnalyticsCreator
Sources and connectors
REST APIs✅Anonymous, Basic, OAuth2 client credentials and any token or API key through free headers; 6 pagination forms; endpoints picked from an OpenAPI specification; nested JSON becomes a table automatically, up to 10 levels deep[26][27]◐REST is listed as a connector on the features and FAQ pages; the documentation describes no REST connector type, and the FAQ describes CSV output or loading 'externally filled tables'[2][1][16]
OData✅OData v2 and v4; anonymous, Basic or OAuth2 (client credentials or authorization code with refresh token)[28][29]✅OData connector[16]
Files✅CSV, Excel and Parquet; from Azure Blob Storage, Azure Data Lake, a file server or SharePoint[30][31][32]✅CSV, Excel and Access; Azure Blob[16]
Databases✅SQL Server, Azure SQL, PostgreSQL, MySQL, Oracle, IBM DB2 and Snowflake[33]✅SQL Server and Oracle; other databases through ODBC or OLE DB[16]
SAP✅Official SAP partner. S/4HANA and SAP ERP through OData services; SAP HANA through XS OData; SAP Analytics Cloud, Datasphere and Business Data Cloud. Without third-party middleware[34][35][36][37]◐SAP ERP and S/4HANA via Theobald Software (listed by the vendor under 'Build-in Connectivity'); SAP Business One own[1]
Microsoft✅SQL Server, Azure SQL, SharePoint, Teams and Microsoft 365 through Microsoft Graph, Azure Blob Storage, Azure Data Lake and Power BI; Dynamics 365 Business Central through OData (OAuth2 sign-in in development)[38][32][39]✅Among others SQL Server, Azure Blob, SharePoint, Excel and Access[16][2]
Oracle✅Oracle Database; Oracle NetSuite is planned for October 2027[40]✅Oracle Database[16]
Dutch business software✅AFAS and Exact Online with their own connection (official partner of both); also TOPdesk and Simplicate[41][42]–Not mentioned on the website or in the documentation; generic access to 250+ sources via CData (third party, ODBC/OLE DB)[1][8]
Type mapping✅Organisation-wide + per source + per column, travels along with changes[43]◐Transformation rules per data type; a mapping table is not described[8]
Loading and history
Load types7: FULL, DELTA, DELTAIMAGE, IMAGE, OVERWRITE, RELOAD, ADDITIONAL[44]5 persist types: Full, Merge, Historical, Incremental, Manual[17]
History (SCD2)✅Default for 6 of the 7 load types, set per table[45]✅SCD 0/1/2 per column, snapshot, gapless[1][9][18]
Rows that disappear from the source✅Keep / close / close within the delta window[44]✅Close / empty record / nothing, plus a delete filter[9]
Change detection✅Hashes (SHA2-512) on key and content. A field can be kept out of the comparison: if only that field changes, the row is not updated and the new value only comes along when another field changes[44]◐Detection based on the primary key; hash-based change detection not described (hash keys exist for Data Vault)[18]
Two delta columns✅12 source types[44]◐The 'Incremental' persisting type uses one column and is, per the docs, 'intended for data that is never changed or deleted'; other deltas via an import filter or historization; a second delta column is not described[17]
Override the load type per run✅"Alternative load"[44]–Not publicly documented
Delta after a failed load✅Watermark only advances after success[44]◐Transactions and 5 automatic retries; the 'Incremental' persisting type detects new rows using the maximum value of the incremental column[10][17]
Roll back a table to a point in time✅Per table to a point in time; dry run via the stored procedure[46]–Not publicly documented; "rollback" refers to transactions and Git versions[4]
Large tables✅Paginated merge, 100M+ rows[44]◐Partition switching, columnstore and compression; BARC users report increasing processing times as the warehouse grows[17][21][25]
Modelling and delivery
Have the model generated (Kimball, Data Vault)❌Deliberately not. Yres delivers the layer with full history; you build the model on top yourself, in the method of your choice[47]✅Kimball, Data Vault 2.0, Inmon, 3NF[1]
Your own model on top of the data✅Any method, from Kimball to Data Vault, is built with your own views and procedures on the history layer. Views can be persisted as tables, and your own objects travel to test and production through changes[48][49]✅Your own scripts; generated code can be modified freely[1][3]
Generate a semantic model❌No✅Power BI, Tableau, Qlik[1]
Lineage◐Object level + impact analysis[50]✅Yes (level of detail not publicly documented)[5]
Data quality / enrichment❌Not as a module◐Rules in pipelines[1]
Target platformsAzure SQL, with a Parquet feed to the Data Lake that Fabric, Databricks and Synapse can read. Microsoft Fabric as a target platform is planned for the end of 2027[59][51]SQL Server, Azure SQL, Synapse, Fabric[7]
MCP server for AIAnnounced, October 2027–Not publicly documented
Changing and deploying
Environments✅Release → import → install, incl. ADF; 1 environment in Essentials, 2 in Advanced, 6 in Ultimate[52][62]✅DACPAC deployment, can be limited to an object group; unlimited environments at no extra cost; backup first, drift blocking, data-loss protection[2][4][19]
Deploy only the change you want✅Per change (changes system: Advanced and up)[53]◐Deployment can be limited to an object group and selected packages; per-change deployment is not described[19]
Changes recorded automatically✅Per change, within a project (changes system: Advanced and up)[53]–Not publicly documented; manual locking, user groups and Git export are available[4]
Block on unreleased dependencies✅Yes (changes system: Advanced and up)[52]–Not described; deployment is blocked when the target has drifted[4]
Impact analysis before installing✅Yes (also: print the SQL first)[52]◐From lineage[5]
Deployment safeguards◐Impact analysis, review the SQL beforehand✅Backup beforehand, blocks on drift, protection against data loss[4]
Name translation per environment✅Rules per environment[54]✅SQLCMD and environment variables[20]
Audit of changes made directly in the database✅Every DDL: who, when, full command[55]◐Drift in the target environment blocks the deployment; who or what changed is not described[4]
Compare versions of an object✅Diff between two versions of an object[49]◐Via Git diff of the JSON export[4]
Compare definitions between environments✅Yes[49]–Not publicly documented
Track source changes✅Source ↔ dictionary ↔ STAGE ↔ HIS; automatic adjustment; REST grows along[43]◐Refreshing the metadata of an existing source is a deliberate design-time action in the wizard[24][8]
Running and operating
Scheduling and orchestration✅Visual master pipeline with 7 building blocks and paths on success and failure. Refreshing your Power BI models is one of those blocks: the report refreshes right after the data has loaded[56]◐Workflow package; scheduling via an external scheduler[2]
Maintenance included✅Four maintenance pipelines (cleanup, log retention, archiving, index maintenance), which you combine into a master pipeline yourself[57]–Not publicly documented; the documentation does describe SQL templates for updating statistics in the historization and persisting procedures[22]
Monitoring per table✅Status, counts, steps, planned/skipped, locks, slow queries[56][55]◐Log tables[11]
Health checks✅About 80 checks in 9 groups; some with a ready-made fix script[54]◐Built-in validation checks the repository for errors or inconsistencies; 'Evaluate' button before saving or deploying[23][8]
Test suite in the product✅±1,655 checks / 195 objects[58]–No built-in test suite mentioned; testing via the partner product BiG EVAL[13]
Scale the database up and down✅From Advanced up, with arbitration between concurrent workflows[59][62]–Not mentioned; the FAQ says throughput is determined by the underlying database and orchestration[2]
Archiving✅Per table, old history moves to Parquet in the Data Lake, by age or by a date field. Deleting from the database only happens after the number of copied rows matches exactly, and is off by default[60]–Not mentioned[8]
Database and Data Lake as one dataset✅One view combines the table in the database with the archive in the Data Lake. Reports keep seeing the full dataset, even after old data has been archived[60]–Not publicly documented
Log retention✅Per log table, dry run[55]–Not mentioned in the vendor's documentation or website (as of 2026-09-21)[11][8]
What you have to run yourselfNo server of its own: Yres runs on Azure Data Factory and Azure SQL. Only for sources inside your own network do you install a Microsoft self-hosted integration runtime[59]Windows desktop client, a repository database and a connection (port 443) to the generation engine; per the Trust page not usable for air-gapped environments[12][4]
Security and ownership
What is stored whereData, metadata, credentials and secrets live in the customer's Azure tenant, in the region you choose yourself. Yres only holds the settings of the web app (Azure West Europe), never customer data or credentials[59][61]Data in your own environment; per the Trust page metadata goes to the vendor's generation engine 'regardless of repository location', hosted in Germany[4]
Secrets✅Key Vault only; managed identity[59]◐Encrypted strings in the repository; Azure AD and service principals mentioned; Key Vault and managed identity not mentioned[4][8]
CertificationsISO 27001. Plainwater, the maker of Yres, is a Microsoft Solutions Partner for Data & AI (Azure)[61]No certification stated on the Trust page; it says: 'Supports alignment with ISO 27001, SOC 2 readiness'[4]
Web application firewall✅In every plan[62]–Not publicly documented
What remains when you stop✅Everything keeps running in your own tenant, and your own data engineers can continue developing on it✅The generated code is yours and remains usable, even after your subscription has expired; confirmed by BARC[14][25]
Getting started, support and cost
Installation (with the right permissions in Azure in place)✅Automated in your own tenant, ±20 min[63]Desktop client + repository; time not stated; BARC: "some complexity in initial setup"[12][25]
DocumentationExtensive knowledge base in Dutch and English, with an AI assistant that answers your questions[64]Documentation in English (website also in German); tutorials and videos[15]
TrainingAcademy: 3 public courses, quizzes, simulators, verifiable certificates[65]–No academy or certification mentioned[8]
The NetherlandsDutch product and team1 partner in the Netherlands (Early Friday)[6]
Price (public)Essentials €350, Advanced €674, Ultimate €997 per month; fixed and public. Per year: €4,200 to €11,964[62]From €800 per month, per named or concurrent user; no consumption pricing. Per year: from €9,600; an upper limit is not public[3]
Ongoing Azure costsSmall default database; scales up only while loading[59]◐Layers are views by default (little storage); a guideline for Azure costs is not described[8]
Independent reviews❌None yetBARC user review 2026 (25 respondents): customer satisfaction 8.7; Business Value 8.3[25]

See the full comparison with all alternatives

Which one fits you?

AnalyticsCreator

Choose AnalyticsCreator if you want the tool to generate the data model for you, in Data Vault 2.0, Kimball, Inmon or 3NF, including a semantic model for Power BI, Tableau or Qlik, and your sources are not AFAS or Exact Online. Unlimited environments at no extra cost and a backup before every deployment count in its favour, as does the 2026 BARC user review. You then work with a Windows desktop client and accept that metadata goes to the vendor's generation engine.

Yres

Choose Yres if your sources must sit reliably and with full history in your own Azure, including AFAS and Exact Online, and you want to keep the model on top in your own hands. Yres deploys per change, also records direct database changes, and operations such as health checks, test suite, scaling and archiving are part of the product. It runs without a server of its own, at a fixed price of €350 to €997 per month.

Frequently asked questions

What is the price difference between Yres and AnalyticsCreator?

Yres costs €350, €674 or €997 per month, fixed and public, depending on the number of sources and environments; not per user. AnalyticsCreator starts, per its own product page (as of 2026-09-22), at €800 per month per named or concurrent user, with no consumption pricing; an upper limit is not public. With both, your own Azure costs come on top.

Does Yres also generate a data model, like AnalyticsCreator?

No, deliberately not. AnalyticsCreator generates the model in Kimball, Data Vault 2.0, Inmon or 3NF plus a semantic model. Yres delivers the layer with full history; on top of it you build the model yourself, in any method, with your own views and procedures that travel to production through changes. You lose no freedom, but you get no tooling for it either.

Can I connect AFAS and Exact Online?

With Yres, yes: its own connectors for both, and Yres is an official partner of AFAS and Exact. For AnalyticsCreator we found AFAS and Exact Online not mentioned on the website or in the documentation on 2026-09-22; there is generic access to 250+ sources through CData, a third party, via ODBC or OLE DB.

What remains if I stop, and where does my metadata live?

With Yres everything keeps running in your own tenant, including new loads, and your own engineers can continue developing; metadata and secrets stay in your tenant. With AnalyticsCreator the generated code remains yours, even after the subscription has expired (confirmed by BARC); metadata goes, per its own Trust page (as of 2026-09-22), to the generation engine in Germany.

Sources

Everything about the other vendor comes from that vendor's own public material or from a named analyst. We only use ❌ when the vendor itself or an open customer request says so; otherwise it reads 'not publicly documented'. Is something incorrect or out of date? Email us: feedback@yres.app.

  1. [1] AnalyticsCreator — Features overview. https://www.analyticscreator.com/features · checked 2026-09-21
  2. [2] AnalyticsCreator — FAQ (REST, runtime, deployment). https://www.analyticscreator.com/faq · checked 2026-09-21
  3. [3] AnalyticsCreator — Product page. https://www.analyticscreator.com/product · checked 2026-09-21
  4. [4] AnalyticsCreator — Trust page (hosting, metadata, certifications, drift, Git). https://www.analyticscreator.com/trust · checked 2026-09-21
  5. [5] AnalyticsCreator — Data lineage. https://www.analyticscreator.com/data-lineage · checked 2026-09-21
  6. [6] AnalyticsCreator — Partners. https://www.analyticscreator.com/partners · checked 2026-09-21
  7. [7] AnalyticsCreator — Platform support. https://www.analyticscreator.com/docs/platform-support · checked 2026-09-21
  8. [8] AnalyticsCreator — Searchable index of the full documentation. https://www.analyticscreator.com/docs/search-index.json · checked 2026-09-21
  9. [9] AnalyticsCreator — Parameters: historization. https://www.analyticscreator.com/docs/reference/parameters/parameters-historization · checked 2026-09-21
  10. [10] AnalyticsCreator — Parameters: synchronization. https://www.analyticscreator.com/docs/reference/parameters/parameters-synchronization · checked 2026-09-21
  11. [11] AnalyticsCreator — Parameters: logging. https://www.analyticscreator.com/docs/reference/parameters/parameters-logging · checked 2026-09-21
  12. [12] AnalyticsCreator — System requirements. https://www.analyticscreator.com/docs/getting-started/system-requirements · checked 2026-09-21
  13. [13] AnalyticsCreator — Data test automation with partner Big EVAL. https://www.analyticscreator.com/video-blog/why-combine-data-warehouse-automation-and-data-test-automation · checked 2026-09-21
  14. [14] AnalyticsCreator — Homepage ("The result is yours"). https://www.analyticscreator.com/ · checked 2026-09-21
  15. [15] AnalyticsCreator — Documentation home. https://www.analyticscreator.com/docs · checked 2026-09-21
  16. [16] AnalyticsCreator — Reference: connector types. https://www.analyticscreator.com/docs/reference/entity-types/connector-types · checked 2026-09-21
  17. [17] AnalyticsCreator — Persisting types and replacement strategies. https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/persisting/persisting-types-and-replacement-strategies · checked 2026-09-21
  18. [18] AnalyticsCreator — User guide: historization. https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/historization · checked 2026-09-21
  19. [19] AnalyticsCreator — Reference: deployment page. https://www.analyticscreator.com/docs/reference/user-interface/pages/pages-deployment · checked 2026-09-21
  20. [20] AnalyticsCreator — Deployment package definition (SQLCMD variables). https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/deployment/create-deployment-package · checked 2026-09-21
  21. [21] AnalyticsCreator — Create and configure a table index (columnstore, compression). https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/indexes/create-configure-indexes · checked 2026-09-21
  22. [22] AnalyticsCreator — Parameters: SQL templates (update statistics). https://www.analyticscreator.com/docs/reference/parameters/parameters-sql-templates · checked 2026-09-21
  23. [23] AnalyticsCreator — User guide: working with AnalyticsCreator (validation, Evaluate). https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator · checked 2026-09-21
  24. [24] AnalyticsCreator — DWH wizard: read metadata from a source. https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/using-dwh-wizard/read-metadata-from-source · checked 2026-09-21
  25. [25] BARC — BARC user review of AnalyticsCreator (2026) (analyst). https://barc.com/review/analyticscreator/ · checked 2026-09-21
  26. [26] Yres — REST service source. https://yres.eu/en/wiki/integraties/bronnen/restservice · checked 2026-09-21
  27. [27] Yres — REST: JSON interpretation. https://yres.eu/en/wiki/integraties/bronnen/restservice-json · checked 2026-09-21
  28. [28] Yres — OData source. https://yres.eu/en/wiki/integraties/bronnen/odata · checked 2026-09-21
  29. [29] Yres — OData with OAuth. https://yres.eu/en/wiki/integraties/bronnen/odata-oauth · checked 2026-09-21
  30. [30] Yres — Azure Blob Storage source (file formats). https://yres.eu/en/wiki/integraties/bronnen/azure-blob-storage · checked 2026-09-21
  31. [31] Yres — File server source. https://yres.eu/en/wiki/integraties/bronnen/file-server · checked 2026-09-21
  32. [32] Yres — SharePoint source. https://yres.eu/en/wiki/integraties/bronnen/sharepoint · checked 2026-09-21
  33. [33] Yres — Integration catalogue (all sources). https://yres.eu/en/wiki/integraties/catalogus · checked 2026-09-21
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  35. [35] Yres — SAP HANA. https://yres.eu/en/wiki/integraties/bronnen/sap-hana · checked 2026-09-21
  36. [36] Yres — SAP Analytics Cloud. https://yres.eu/en/wiki/integraties/bronnen/sac · checked 2026-09-21
  37. [37] Yres — SAP Datasphere. https://yres.eu/en/wiki/integraties/bronnen/sap-datasphere · checked 2026-09-21
  38. [38] Yres — Microsoft Graph. https://yres.eu/en/wiki/integraties/bronnen/microsoft-graph · checked 2026-09-21
  39. [39] Yres — Dynamics 365. https://yres.eu/en/wiki/integraties/bronnen/dynamics-365 · checked 2026-09-21
  40. [40] Yres — Oracle. https://yres.eu/en/wiki/integraties/bronnen/oracle · checked 2026-09-21
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  42. [42] Yres — Exact Online. https://yres.eu/en/wiki/integraties/bronnen/exact-online · checked 2026-09-21
  43. [43] Yres — Data sources screen (type mapping, metadata compare). https://yres.eu/en/wiki/frontend/data-sources · checked 2026-09-21
  44. [44] Yres — Load types. https://yres.eu/en/wiki/concepten/load-types · checked 2026-09-21
  45. [45] Yres — History (SCD2). https://yres.eu/en/wiki/concepten/historie-scd2 · checked 2026-09-21
  46. [46] Yres — Rollback and reset. https://yres.eu/en/wiki/concepten/rollback-reset · checked 2026-09-21
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  48. [48] Yres — SQL interaction. https://yres.eu/en/wiki/referentie/sql-interaction · checked 2026-09-21
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  52. [52] Yres — CI/CD and DTAP. https://yres.eu/en/wiki/architectuur/cicd-dtap · checked 2026-09-21
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Data warehouse automation that runs in your own Azure

Built for organisations on Azure and Power BI. Yres connects your sources, keeps the history and promotes changes in a controlled way, without manual work.

Talk to a data architectSee how it works