Yres
HomeFeaturesConnectorsPricingCompareCustomer storiesFAQ
NL|EN
Talk to a data architect
Yres

Data stays in your own Azure tenant

Address

Friesestraatweg 219 9743 AD Groningen

Contact

info@yres.app
+31 85 130 3905

About us·Microsoft Marketplace

Product

  • Features
  • Integrations
  • Pricing
  • FAQ

Background

  • What is data warehouse automation?
  • Why not just Azure Data Factory?
  • Compared with other tools
  • Yres compared with TimeXtender
  • Yres compared with AnalyticsCreator
  • Yres or building it yourself on Azure
  • A data foundation for AI
  • Yres and Microsoft Fabric

Who it is for

  • Housing associations
  • Food and manufacturing
  • Partners
  • Customer stories

Service

  • Wiki
  • Academy
  • Log in
  • Talk to a data architect

© 2026 Yres. Yres Oog op Data

Privacy PolicyIRIS is now Yres

Yres, TimeXtender or AnalyticsCreator? The honest comparison

You do not choose a data warehouse platform for the demo, but for the years after it. Here you see at a glance where Yres makes the difference, where another option is stronger, and which questions to put to every vendor.

  • AFAS, Exact Online and SAP, without detours

    An official partner of all three. AFAS and Exact Online have a dedicated connection; SAP connects through SAP's own standards, without third-party middleware.

  • Everything in your own Azure

    Data, pipelines and keys live in your tenant. Yres runs on Azure Data Factory and Azure SQL and needs no server of its own.

  • Change with a safety net

    Every edit is recorded and moves to production in a controlled way. Even what someone changes directly in the database shows up.

  • Fixed price from €350 per month

    Three packages, public and known upfront. No price per user and no consumption charges from Yres.

Discuss your situation with a data architect

Everything we say about another vendor comes from their own public material, with the source attached. So you can check it yourself. Last checked: 2026-09-21.

  1. Which one fits you?
  2. The comparison at a glance
  3. Where Yres makes the difference
  4. Sources and accountability

Which one fits you?

  • Choose AnalyticsCreator if you want the tool to generate the data model for you, in Data Vault or Kimball, including a semantic model for Power BI, and your sources are not AFAS or Exact Online.
  • Choose TimeXtender if you want a broad suite with data quality, enrichment and orchestration, several target platforms, an MCP server in Early Access and a large Dutch partner network, and you have the infrastructure and budget for it.
  • Build it yourself if you have a strong team of your own, few sources, and complete freedom matters more than lead time and maintenance load.
  • Choose Yres if AFAS, Exact Online, SAP and your other sources must sit reliably and with history in your own Azure, you want changes rolled out in a controlled way, you want to keep the model on top in your own hands, and you want to run it with a small team at a fixed price.

The comparison at a glance

Yres next to AnalyticsCreator, TimeXtender and building it yourself, on the points that matter in practice. The numbers in brackets refer to the sources at the bottom.

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

YresAnalyticsCreatorTimeXtenderBuild it yourself (dbt + ADF)
Sources and connectorsAll three connect REST, OData, files and the common databases. Yres reads the most database types itself and turns nested JSON into a table automatically; TimeXtender offers the most REST authentication methods. For SAP, Yres uses SAP's own standards, AnalyticsCreator a third-party connector. Yres is an official partner of AFAS, Exact Online and SAP, and is the only one with a dedicated connection for both AFAS and Exact Online.
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[72][73]◐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][15]✅None, Basic, Bearer, OAuth2, OAuth refresh token, Authentication endpoint and Azure service principal; configurable pagination; OpenAPI import; nested JSON through XSLT table flattening[51]Build it yourself
OData✅OData v2 and v4; anonymous, Basic or OAuth2 (client credentials or authorization code with refresh token)[74][75]✅OData connector[15]✅Own OData data source[31]Build it yourself
Files✅CSV, Excel and Parquet; from Azure Blob Storage, Azure Data Lake, a file server or SharePoint[76][77][78]✅CSV, Excel and Access; Azure Blob[15]✅CSV, Excel, XML/JSON and Parquet (own providers)[31]Build it yourself
Databases✅SQL Server, Azure SQL, PostgreSQL, MySQL, Oracle, IBM DB2 and Snowflake[79]✅SQL Server and Oracle; other databases through ODBC or OLE DB[15]✅Among others SQL Server, MySQL and Oracle (own providers)[31]Build it yourself
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[80][81][82][83]◐SAP ERP and S/4HANA via Theobald Software (listed by the vendor under 'Build-in Connectivity'); SAP Business One own[1]✅Own SAP Table data source[31]ADF connectors
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)[84][78][85]✅Among others SQL Server, Azure Blob, SharePoint, Excel and Access[15][2]✅Among others SQL Server, Excel and Dynamics 365 Business Central (own providers)[31]ADF connectors
Oracle✅Oracle Database; Oracle NetSuite is planned for October 2027[86]✅Oracle Database[15]✅Oracle Database (own provider)[31]ADF connectors
Dutch business software✅AFAS and Exact Online with their own connection (official partner of both); also TOPdesk and Simplicate[87][88]–Not mentioned on the website or in the documentation; generic access to 250+ sources via CData (third party, ODBC/OLE DB)[1][8]◐Exact Online as an own data source; for AFAS a partner guide (May 2024) describes connecting via the generic REST source[32][33]Build it yourself
Type mapping✅Organisation-wide + per source + per column, travels along with changes[89]◐Transformation rules per data type; a mapping table is not described[8]✅'Override Data Type' rules per data source and per field[42]Build it yourself
Loading and historyAll three keep history and can load incrementally. Yres has the most load variants and is the only one for which we found per-table rollback documented. All three have provisions for very large tables.
Load types7: FULL, DELTA, DELTAIMAGE, IMAGE, OVERWRITE, RELOAD, ADDITIONAL[90]5 persist types: Full, Merge, Historical, Incremental, Manual[16]3 modes: Automatic, Incremental, Full[53]Build it yourself
History (SCD2)✅Default for 6 of the 7 load types, set per table[91]✅SCD 0/1/2 per column, snapshot, gapless[1][9][17]✅Type 0/I/II per field[34]Build it yourself
Rows that disappear from the source✅Keep / close / close within the delta window[90]✅Close / empty record / nothing, plus a delete filter[9]◐Soft deletes (tombstone) supported; per the sources below hard deletes cannot be combined with history tables, and incremental loading over REST cannot handle updates and deletes[52][54]Build it yourself
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[90]◐Detection based on the primary key; hash-based change detection not described (hash keys exist for Data Vault)[17]✅Hash keys for SCD I/II; SHA-1 default, SHA-2 512 optional[55][38]Build it yourself
Two delta columns✅12 source types[90]◐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[16]◐Multiple incremental rules[52]Build it yourself
Override the load type per run✅"Alternative load"[90]–Not publicly documented✅A full load can be forced per table or per Ingest task[53][39]Build it yourself
Delta after a failed load✅Watermark only advances after success[90]◐Transactions and 5 automatic retries; the 'Incremental' persisting type detects new rows using the maximum value of the incremental column[10][16]–Not publicly documentedBuild it yourself
Large tables✅Paginated merge, 100M+ rows[90]◐Partition switching, columnstore and compression; BARC users report increasing processing times as the warehouse grows[16][20][24]✅Batch data cleansing, table partitioning and compression[40][41]Build it yourself
Modelling and deliveryAnalyticsCreator and TimeXtender generate the model for you, in methods such as Kimball and Data Vault, including a semantic model for Power BI. Yres deliberately does not: modelling depends strongly on your own preferences and experience, and changes along with what your reporting tools need. Yres delivers the layer with full history on which any method fits; so you lose no freedom, but you get no tooling for it. Lineage and data quality are also further developed in the other two.
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[92]✅Kimball, Data Vault 2.0, Inmon, 3NF[1]Dimensional; Data Vault not publicly documented[26]Free choice
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[93][94]✅Your own scripts; generated code can be modified freely[1][3]✅Your own views and procedures[59]✅Yes
Generate a semantic model❌No✅Power BI, Tableau, Qlik[1]✅Power BI, Tableau, Qlik[26]Build it yourself
Lineage◐Object level + impact analysis[95]✅Yes (level of detail not publicly documented)[5]✅Field-level lineage and impact documentation[35]Build it yourself
Data quality / enrichment❌Not as a module◐Rules in pipelines[1]✅Separate products[25][27]Build it yourself
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[103][96]SQL Server, Azure SQL, Synapse, Fabric[7]Azure, Fabric, SQL Server, Snowflake, AWS[26]Free choice
MCP server for AIAnnounced, October 2027–Not publicly documented✅MCP Server 2.0 available via Early Access (May 2026); Xpilot Analytics in Private Preview[28][37]❌No
Changing and deployingAll three support multiple environments. Yres deploys per change and also records what someone changes directly in the database. The other two have more safeguards around the deployment itself, such as a backup beforehand.
Environments✅Release → import → install, incl. ADF; 1 environment in Essentials, 2 in Advanced, 6 in Ultimate[97][106]✅DACPAC deployment, can be limited to an object group; unlimited environments at no extra cost; backup first, drift blocking, data-loss protection[2][4][18]✅Migration of the whole instance with preview; restore a version[36]Build it yourself
Deploy only the change you want✅Per change (changes system: Advanced and up)[98]◐Deployment can be limited to an object group and selected packages; per-change deployment is not described[18]❌Per the source below: 'promotion of individual objects within instances is not yet supported'; customer requests for this are still open[65][66]Via Git
Changes recorded automatically✅Per change, within a project (changes system: Advanced and up)[98]–Not publicly documented; manual locking, user groups and Git export are available[4]◐Work items are created manually ('like Post-it notes'); automatic version notes are an open customer request (May 2026)[43][67]❌No
Impact analysis before installing✅Yes (also: print the SQL first)[97]◐From lineage[5]◐Review changes before transfer[36]❌No
Deployment safeguards◐Impact analysis, review the SQL beforehand✅Backup beforehand, blocks on drift, protection against data loss[4]✅Preview, restore a version[36]Build it yourself
Name translation per environment✅Rules per environment[99]✅SQLCMD and environment variables[19]◐Mapping of sources and endpoints[36]Build it yourself
Audit of changes made directly in the database✅Every DDL: who, when, full command[100]◐Drift in the target environment blocks the deployment; who or what changed is not described[4]–Not publicly documentedBuild it yourself
Compare versions of an object✅Diff between two versions of an object[94]◐Via Git diff of the JSON export[4]◐Per the source below: open a previous instance version to compare; a diff feature is an open customer request[68]Via Git
Track source changes✅Source ↔ dictionary ↔ STAGE ↔ HIS; automatic adjustment; REST grows along[89]◐Refreshing the metadata of an existing source is a deliberate design-time action in the wizard[23][8]✅The 'Import Metadata' task synchronizes the structure of the source with the metadata stored in the Ingest instance[39]Build it yourself
Running and operatingYres ships the most operations in the product: maintenance, health checks, a test suite, scaling and archiving. TimeXtender offers part of that through Orchestration and has the most extensive execution monitor. AnalyticsCreator is a design tool without a runtime of its own; running happens on the target platform.
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[101]◐Workflow package; scheduling via an external scheduler[2]◐In-product Jobs are deprecated; scheduling runs via TimeXtender Orchestration (Lite version included)[49][25]Build it yourself
Maintenance included✅Four maintenance pipelines (cleanup, log retention, archiving, index maintenance), which you combine into a master pipeline yourself[102]–Not publicly documented; the documentation does describe SQL templates for updating statistics in the historization and persisting procedures[21]◐Index automation, automatic log truncation and a storage management task[45][39]❌No
Monitoring per table✅Status, counts, steps, planned/skipped, locks, slow queries[101][100]◐Log tables[11]✅Execution log with counts and steps; Gantt view to compare two executions[57]Build it yourself
Health checks✅About 80 checks in 9 groups; some with a ready-made fix script[99]◐Built-in validation checks the repository for errors or inconsistencies; 'Evaluate' button before saving or deploying[22][8]◐'Performance Recommendations': 8 recommendations applied with one click; a broader analyzer is an open customer request (2024)[44][70]❌No
Scale the database up and down✅From Advanced up, with arbitration between concurrent workflows[103][106]–Not mentioned; the FAQ says throughput is determined by the underlying database and orchestration[2]◐Via Orchestration's Azure Cloud Optimizer package; per the source below not in the Lite version; the reference architecture names PowerShell as the alternative[50][47]Build it yourself
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[104]–Not mentioned[8]◐The storage management task moves old Ingest versions to 'cool' storage (with Azure Data Lake storage); archiving of history tables is not documented[39][69]Build it yourself
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[104]–Not publicly documented–Not publicly documentedBuild it yourself
Log retention✅Per log table, dry run[100]–Not mentioned in the vendor's documentation or website (as of 2026-09-21)[11][8]◐Ingest service log default 7 days; execution log default 90 days, adjustable[56][58]Build it yourself
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[103]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]Per the Azure reference architecture (2023) a VM for the Ingest and Execution services that 'must remain running for TimeXtender Data Integration to function'; plus a Windows desktop client and the vendor's portal[47][30]Everything
Security and ownershipWith all three your data stays in your own environment. TimeXtender holds the most certifications. Yres keeps secrets exclusively in Key Vault and works with managed identity. With Yres and AnalyticsCreator your environment keeps working if you stop.
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[103][105]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]Data in your own environment; metadata and portal at the vendor[46][30]Your own environment
Secrets✅Key Vault only; managed identity[103]◐Encrypted strings in the repository; Azure AD and service principals mentioned; Key Vault and managed identity not mentioned[4][8]❌Per the sources below no managed identity for Azure SQL storage; Entra service principal is supported, but not together with ADF; customer requests for this are open[61][62][63][64]Build it yourself
CertificationsISO 27001. Plainwater, the maker of Yres, is a Microsoft Solutions Partner for Data & AI (Azure)[105]No certification stated on the Trust page; it says: 'Supports alignment with ISO 27001, SOC 2 readiness'[4]✅SOC 2 Type II and ISO 27001:2022[48]N/a
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[13][24]–Not publicly documentedEverything
Getting started, support and costYres has the lowest entry price and documentation in Dutch. TimeXtender has the largest partner network in the Netherlands, a free academy with exam and independent reviews, which Yres does not have yet.
Installation (with the right permissions in Azure in place)✅Automated in your own tenant, ±20 min[107]Desktop client + repository; time not stated; BARC: "some complexity in initial setup"[12][24]Server, services and desktop client[30]Weeks
DocumentationExtensive knowledge base in Dutch and English, with an AI assistant that answers your questions[108]Documentation in English (website also in German); tutorials and videos[14]English-language knowledge base, active community[30]—
TrainingAcademy: 3 public courses, quizzes, simulators, verifiable certificates[109]–No academy or certification mentioned[8]✅Free online training; 75-minute certification exam, pass mark 80%[60]—
The NetherlandsDutch product and team1 partner in the Netherlands (Early Friday)[6]11 partners based in the Netherlands in the partner finder[29]N/a
Price (public)Essentials €350, Advanced €674, Ultimate €997 per month; fixed and public. Per year: €4,200 to €11,964[106]From €800 per month, per named or concurrent user; no consumption pricing. Per year: from €9,600; an upper limit is not public[3]Starter €34,000, Standard €45,000, Premium €76,000, Enterprise €150,000, including Orchestration Lite. Per year: €34,000 to €150,000[25]No licence; the cost is in engineers' hours. Per year: depends on the hours spent on building and maintenance
Ongoing Azure costsSmall default database; scales up only while loading[103]◐Layers are views by default (little storage); a guideline for Azure costs is not described[8]Reference architecture: DS2_v2 VM; production Hyperscale from 4 vCores; development serverless[47]Depends on your own setup
Independent reviews❌None yetBARC user review 2026 (25 respondents): customer satisfaction 8.7; Business Value 8.3[24]BARC Data Fabric Survey 26: first in Recommendation (9.3) and Ease of Use (8.5) in the DWH Automation and Data Engineering Tools peer groups[71]N/a

Where Yres makes the difference

A feature list says little about how your data warehouse behaves after three years. The difference is in the daily work: loading, changing and operating. Six reasons teams choose Yres, each with the questions you can put to any vendor. Including us.

Loading you can rely on

Full history, smart deltas, and a mistake you roll back per table to the moment you choose. So your figures stay right, even when something goes wrong.

  • Yres has seven load types: FULL, DELTA, DELTAIMAGE, IMAGE, OVERWRITE, RELOAD and ADDITIONAL. Six of them keep the full SCD2 history; only OVERWRITE starts from scratch every time.
  • The load type decides what happens to rows that disappear from the source: FULL leaves them in place, IMAGE closes them as a soft delete and DELTAIMAGE does so only within the delta window.
  • RELOAD closes the current generation of rows and loads the full set again without discarding history; ADDITIONAL only appends and is meant for logs and events.
  • Changes are detected with two SHA2-512 hashes, one on the key and one on the content, and you can exclude individual columns from the comparison.
  • A delta load works with one or two delta columns, where the highest value counts and NULL is handled safely; this applies to the SQL sources and, since version 1.56, also to Salesforce, SAP Analytics Cloud and AFAS.
  • The watermark only moves forward after a fully successful load, so a failed delta is fetched again automatically on the next run and there is nothing for you to repair.
  • You can choose a different load type per run, for example a delta on weekdays and a full check with IMAGE at the weekend.
  • You can roll back a single table to a point in time without touching the rest: newer rows are removed, closed versions are reopened and the watermark is reset, with a dry run that first only shows the script.
  • Merging into the history table runs in pages and is built for tables of more than 100 million rows; a duplicate request for the same table is skipped rather than loaded twice.

Put these questions to every vendor

  • What happens to rows that disappear from the source?
  • Can I restore a single table to yesterday at 14:00 without touching the rest?
  • What happens to my delta when a load fails halfway through?

All your sources, without detours

AFAS, Exact Online and SAP connect without a third-party layer in between. And if your system is not on the list, there is REST and OData.

  • Yres is an official partner of AFAS, Exact Online and SAP. AFAS and Exact Online have dedicated connectors; SAP runs through SAP's own standards.
  • For SAP there are dedicated source types for SAP Analytics Cloud (since 1.51) and SAP Business Data Cloud (since 1.53, with a fixed delta column).
  • You connect S/4HANA through the OData services of the SAP Gateway, so through SAP's own standards and without separate SAP middleware or a third-party extractor licence.
  • The generic REST connector offers three authentication methods (anonymous, Basic and OAuth2 client credentials) plus freely configurable HTTP headers.
  • The same REST connector supports six pagination styles: none, Link header per RFC 5988, next link in the body, offset, page number and offset per page.
  • If the API has an OpenAPI 3 specification, you pick the endpoints in the web app instead of typing them in by hand.
  • JSON becomes a table without field mapping: Yres finds the array of records itself, flattens nested objects up to ten levels deep and turns new fields into new columns automatically.
  • OData v2 and v4 are supported with OAuth (client credentials or authorization code with refresh token), and a failed token refresh cannot overwrite a working token.
  • You set type mapping at two levels, for the whole organisation and per source, with an override per column, and those settings travel to test and production through changes.

Put these questions to every vendor

  • Is the connector for my ERP your own, or does it come from a third party with its own licence?
  • What do I do if my API uses a pagination style your connector does not know?

Change with a safety net

A data warehouse changes every week. With Yres you know what changed and who changed it, and it reaches production in a controlled way.

  • Every modification in the development environment is recorded automatically under a change, and every change belongs to a project; the changes system is part of the packages from Advanced up.
  • Deployment takes three steps, release, import and install, across up to six environments (DEV, TST, ACC, SND, PRE and PRD; one in Essentials, two in Advanced, six in Ultimate) and including the Azure Data Factory publish.
  • A release is blocked as long as the change depends on changes that have not been released yet, and you can first run an install as an impact analysis or as printable SQL.
  • Rules per environment translate object names along the way, for example from ERP_DEV to ERP_TST to ERP, so the same change hits the right objects everywhere.
  • Anything done in the database outside Yres is recorded as well: a database trigger writes every DDL change, with who, when, which object and the full command, to a log.
  • In the Database objects screen you compare versions of an object, compare the same object between environments and see the change history per object.
  • You add a custom object to a change together with its dependencies, so that your own views and procedures reach production through the same route.
  • Yres compares source metadata with the dictionary, with STAGE and with HIS and reports missing columns, type differences and removed columns; REST schemas grow along automatically.
  • New product versions arrive as a DACPAC with version-gated migrations for the database and through Azure DevOps for Azure Data Factory, and you update them per environment from the web app.

Put these questions to every vendor

  • If an administrator changes a view directly on production, will I see that, including the difference?
  • Can I put the definition of a single object on test and on production side by side?

Less upkeep, more oversight

After go-live the real work starts. Scheduling, monitoring, clean-up and scaling are in the product, not in scripts of your own.

  • In the visual master pipeline you combine seven building blocks, including loading sources, waiting, changing the database tier and refreshing Power BI, each with a path on success, on failure and on completion; Yres turns it into a regular Azure Data Factory pipeline.
  • Yres ships four maintenance pipelines that you chain in one master pipeline: cleaning up stuck runs, log retention, archiving and index maintenance with a metadata check.
  • Monitoring shows the status, load type, duration and the number of copied, new and changed rows per table, with the steps and a link through to Azure Data Factory.
  • Since version 1.56 you also see planned and skipped loads, and there are separate screens for locks and slow queries plus ready-made SQL views on the log tables.
  • About 80 health checks in nine groups verify the configuration, from duplicates and orphans to missing configuration and licence, and show the severity in the web app and, for some checks, a fix script.
  • Every version includes a test suite of about 1,655 checks across 195 database objects, which works with its own test data, proves its clean-up and can abort on production when loads are running.
  • From the Advanced package up, the database scales up around a load and back down as soon as no work is running, and concurrent workflows are coordinated so that one cannot scale down while another is still running.
  • You configure archiving to Parquet per table, for closed versions or for everything older than a period on a date column; rows are only deleted after an exact count of what was copied, and deletion is off by default.
  • Log retention is configurable per log table (90 to 365 days by default) with a dry run first, and the last run per load and everything running or planned is always kept.

Put these questions to every vendor

  • What runs at night besides my loads, and who maintains it?
  • How do I know my configuration is still correct after three years?
  • What do I pay for the database when nothing is being loaded?

Your data, your Azure

Everything runs in your own tenant, with your keys in your own vault. And if you ever stop using Yres, your environment simply keeps running.

  • Everything runs in your own Azure tenant: Azure Data Factory, the database, Key Vault and optionally a Data Lake.
  • Secrets live only in Key Vault, and Azure Data Factory reaches Key Vault and the Data Lake with a managed identity instead of stored passwords.
  • Yres is ISO 27001 certified, and a web application firewall is included in every package.
  • The web app holds configuration only, in Azure West Europe, and never customer data or credentials; SSO can be enforced and there are roles, an audit log and alerts for expiring credentials.
  • In the database, consumers get rights on decoupling views in the Exposed schema, a read role hides STAGE and the system tables, and the security settings are closed by default.
  • The SQL surface is documented: 111 procedures, 66 functions and 51 views are described, and Yres can still be operated through SQL, even without the web app.
  • You put your own objects in a dedicated schema, CustomYres, with an official extension hook, and they travel to the other environments through changes.
  • If you stop using Yres, your environment simply keeps running, including loading new data, and your own data engineers can continue developing on it.

Put these questions to every vendor

  • Is any of my data or any of my credentials stored on your side?
  • Am I allowed and able to write my own SQL against the data warehouse without losing support?

Start fast, learn it yourself, fixed price

In about twenty minutes a production environment stands in your own tenant. Your team learns it through the knowledge base and the Academy, and you know the cost upfront.

  • In about 20 minutes you have an automatically provisioned production environment in your own tenant, started from a single-use invitation link; the exact duration depends on the number of environments.
  • The installation works on every Azure SQL DTU service tier, including the smaller ones.
  • There is an extensive knowledge base in Dutch and English, with an AI assistant that answers your questions.
  • The academy has three public courses (Basic with 14 lessons, Advanced with 13 lessons and an update course for 1.56) with 38 quizzes, simulators and guided tours.
  • Academy certificates can be verified publicly and are tracked per product version.
  • Prices are fixed and public: Essentials €350, Advanced €674 and Ultimate €997 per month, depending on the number of sources and environments; the changes system and automatic scaling are part of the packages from Advanced up.
  • The Azure costs sit in your own resource group, where you track them per environment in Azure.
  • Those costs stay low thanks to a small default database that only scales up during loads and by archiving old data to cheap storage.

Put these questions to every vendor

  • How long does it take before the first source loads?
  • Can my own team learn this without a certified partner?
  • What does the environment cost per month when nothing changes?

Compared one to one

  • TimeXtender →
  • AnalyticsCreator →
  • Build it yourself (dbt + ADF) →
  • Microsoft Fabric →

Sources and accountability

AnalyticsCreator and TimeXtender are trademarks of their owners. The information about them was gathered with care from their own public documentation, product pages and communities and may have changed since; always consult the vendor. Where a cell about AnalyticsCreator says 'not mentioned', the documentation index (659 pages) and the fetched pages of the website were searched on 2026-09-21 for: key vault, managed identity, afas, exact online, academy, openapi, swagger, parquet. We only use ❌ when the vendor itself or an open customer request says so; otherwise it reads 'not publicly documented'. The information about Yres comes from our knowledge base.

Is something incorrect or out of date? Let us know and we will correct it: 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 — Homepage ("The result is yours"). https://www.analyticscreator.com/ · checked 2026-09-21
  14. [14] AnalyticsCreator — Documentation home. https://www.analyticscreator.com/docs · checked 2026-09-21
  15. [15] AnalyticsCreator — Reference: connector types. https://www.analyticscreator.com/docs/reference/entity-types/connector-types · checked 2026-09-21
  16. [16] 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
  17. [17] AnalyticsCreator — User guide: historization. https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/historization · checked 2026-09-21
  18. [18] AnalyticsCreator — Reference: deployment page. https://www.analyticscreator.com/docs/reference/user-interface/pages/pages-deployment · checked 2026-09-21
  19. [19] AnalyticsCreator — Deployment package definition (SQLCMD variables). https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator/deployment/create-deployment-package · checked 2026-09-21
  20. [20] 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
  21. [21] AnalyticsCreator — Parameters: SQL templates (update statistics). https://www.analyticscreator.com/docs/reference/parameters/parameters-sql-templates · checked 2026-09-21
  22. [22] AnalyticsCreator — User guide: working with AnalyticsCreator (validation, Evaluate). https://www.analyticscreator.com/docs/user-guide/working-with-analyticscreator · checked 2026-09-21
  23. [23] 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
  24. [24] BARC — BARC user review of AnalyticsCreator (2026) (analyst). https://barc.com/review/analyticscreator/ · checked 2026-09-21
  25. [25] TimeXtender — Pricing (pricing page). https://www.timextender.com/pricing · checked 2026-09-21
  26. [26] TimeXtender — Data Integration product page. https://www.timextender.com/data-integration · checked 2026-09-21
  27. [27] TimeXtender — Data Enrichment product page. https://www.timextender.com/data-enrichment · checked 2026-09-21
  28. [28] TimeXtender — MCP Server. https://www.timextender.com/mcp-server · checked 2026-09-21
  29. [29] TimeXtender — Find a partner. https://www.timextender.com/partners/find-a-partner/ · checked 2026-09-21
  30. [30] TimeXtender — Get started with TimeXtender Data Integration. https://support.timextender.com/initial-setup-start-here-115/get-started-with-timextender-data-integration-794 · checked 2026-09-21
  31. [31] TimeXtender — Enhanced data source providers replacing CData providers. https://support.timextender.com/data-sources-112/timextender-enhanced-data-source-providers-to-replace-cdata-providers-3250 · checked 2026-09-21
  32. [32] TimeXtender — Exact Online data source. https://support.timextender.com/data-sources-112/timextender-exact-online-data-source-1807 · checked 2026-09-21
  33. [33] TimeXtender — Connecting to AFAS with the REST connector (community). https://support.timextender.com/tips-tricks-and-best-practices-32/connecting-to-afas-with-the-tx-rest-connector-2110 · checked 2026-09-21
  34. [34] TimeXtender — Use history to implement slowly changing dimensions. https://support.timextender.com/prepare-108/use-history-to-implement-slowly-changing-dimensions-998 · checked 2026-09-21
  35. [35] TimeXtender — Lineage and visualization. https://support.timextender.com/documentation-and-lineage-114/lineage-and-visualization-827 · checked 2026-09-21
  36. [36] TimeXtender — Migrate instances across environments. https://support.timextender.com/add-and-configure-instances-127/migrate-instances-across-environments-1527 · checked 2026-09-21
  37. [37] TimeXtender — MCP 2.0 and Xpilot Analytics early access. https://support.timextender.com/setup-configuration-168/timextender-mcp-2-0-xpilot-analytics-early-access-3971 · checked 2026-09-21
  38. [38] TimeXtender — Custom hash fields. https://support.timextender.com/prepare-108/custom-hash-fields-1036 · checked 2026-09-21
  39. [39] TimeXtender — Tasks in an Ingest instance. https://support.timextender.com/ingest-107/tasks-in-an-ingest-instance-704 · checked 2026-09-21
  40. [40] TimeXtender — Batch data cleansing. https://support.timextender.com/prepare-108/batch-data-cleansing-1035 · checked 2026-09-21
  41. [41] TimeXtender — How to configure table partitioning. https://support.timextender.com/prepare-108/how-to-configure-table-partitioning-1525 · checked 2026-09-21
  42. [42] TimeXtender — Using the override data type feature. https://support.timextender.com/ingest-107/using-the-override-data-type-feature-656 · checked 2026-09-21
  43. [43] TimeXtender — Using work items as part of team development. https://support.timextender.com/prepare-108/using-work-items-as-part-of-team-development-980 · checked 2026-09-21
  44. [44] TimeXtender — Performance recommendations. https://support.timextender.com/prepare-108/performance-recommendations-1021 · checked 2026-09-21
  45. [45] TimeXtender — Indexes. https://support.timextender.com/prepare-108/indexes-1138 · checked 2026-09-21
  46. [46] TimeXtender — Portal security. https://support.timextender.com/managing-your-account-162/portal-security-947 · checked 2026-09-21
  47. [47] TimeXtender — Azure SQL Database reference architecture. https://support.timextender.com/reference-architectures-125/azure-sql-database-reference-architecture-803 · checked 2026-09-21
  48. [48] TimeXtender — Security and compliance. https://www.timextender.com/security · checked 2026-09-21
  49. [49] TimeXtender — Scheduling executions using Jobs (deprecated). https://support.timextender.com/deprecated-154/scheduling-executions-using-jobs-722 · checked 2026-09-21
  50. [50] TimeXtender — Configuring the Azure Cloud Optimizer package (article and author replies). https://support.timextender.com/timextender-orchestration-139/configuring-azure-cloud-optimizer-package-2454 · checked 2026-09-21
  51. [51] TimeXtender — TimeXtender REST data source. https://support.timextender.com/data-sources-112/timextender-rest-data-source-1660 · checked 2026-09-21
  52. [52] TimeXtender — Incremental load in an Ingest instance. https://support.timextender.com/ingest-107/incremental-load-in-an-ingest-instance-708 · checked 2026-09-21
  53. [53] TimeXtender — Incremental load in Prepare instances. https://support.timextender.com/prepare-108/incremental-load-in-prepare-instances-710 · checked 2026-09-21
  54. [54] TimeXtender — Settings related to incremental load (community answer, Sep 2025) (community). https://support.timextender.com/prepare-90/settings-related-to-incremental-load-3641 · checked 2026-09-21
  55. [55] TimeXtender — Instances and settings. https://support.timextender.com/initial-setup-start-here-115/instances-and-settings-1014 · checked 2026-09-21
  56. [56] TimeXtender — Execution queue, logs and statistics. https://support.timextender.com/ingest-107/execution-queue-logs-and-statistics-1005 · checked 2026-09-21
  57. [57] TimeXtender — Execution performance monitoring and troubleshooting. https://support.timextender.com/executions-110/execution-performance-monitoring-and-troubleshooting-624 · checked 2026-09-21
  58. [58] TimeXtender — Repository cleanup. https://support.timextender.com/general-104/repository-cleanup-1355 · checked 2026-09-21
  59. [59] TimeXtender — Customized code and custom scripting. https://support.timextender.com/prepare-108/customized-code-and-custom-scripting-1145 · checked 2026-09-21
  60. [60] TimeXtender — Certification exam study guide. https://support.timextender.com/initial-setup-start-here-115/certification-exam-study-guide-703 · checked 2026-09-21
  61. [61] TimeXtender — Azure SQL managed identity authentication (staff answer, Apr 2024) (vendor answer in the community). https://support.timextender.com/prepare-90/azure-sql-managed-identity-authentication-2072 · checked 2026-09-21
  62. [62] TimeXtender — Service principal access to Azure SQL with ADF (staff answer, Jul 2024; converted to an idea, Oct 2025) (vendor answer in the community). https://support.timextender.com/ideas/service-principal-access-to-dwh-azure-sql-db-with-adf-2264 · checked 2026-09-21
  63. [63] TimeXtender — Idea: add support for Azure SQL managed identity authentication (open customer request). https://support.timextender.com/ideas/add-support-for-azure-sql-managed-identity-authentication-2073 · checked 2026-09-21
  64. [64] TimeXtender — Idea: Prepare instance Azure SQL storage, no option for managed identity (open customer request). https://support.timextender.com/ideas/prepare-instance-azure-sql-database-server-storage-type-authentication-no-option-for-azure-managed-identiy-3903 · checked 2026-09-21
  65. [65] TimeXtender — Idea: partial release of single components (with staff reply, Sep 2024) (open customer request). https://support.timextender.com/ideas/partial-release-deploy-single-multiple-component-between-environments-instead-of-promoting-the-whole-instance-467 · checked 2026-09-21
  66. [66] TimeXtender — Idea: selective environment transfer (partial release) (open customer request). https://support.timextender.com/ideas/selective-environment-transfer-partial-release-1211 · checked 2026-09-21
  67. [67] TimeXtender — Idea: structured, automated or suggested version notes (open customer request). https://support.timextender.com/ideas/structured-automated-suggested-version-notes-in-tdi-3949 · checked 2026-09-21
  68. [68] TimeXtender — Idea: view previous versions (Oct 2025) (open customer request). https://support.timextender.com/ideas/view-previous-versions-3688 · checked 2026-09-21
  69. [69] TimeXtender — Idea: extend the storage management task (cold storage and archive rules) (open customer request). https://support.timextender.com/ideas/extend-storage-management-task-features-for-azure-data-lake-in-ingest-server-move-data-to-cold-storage-archive-based-on-rules-3396 · checked 2026-09-21
  70. [70] TimeXtender — Idea: best practice analyzer for Prepare and Deliver instances (open customer request). https://support.timextender.com/ideas/best-practice-analyzer-for-prepare-and-deliver-instances-2383 · checked 2026-09-21
  71. [71] BARC — BARC review of TimeXtender (Data Fabric Survey 26) (analyst). https://barc.com/review/timextender/ · checked 2026-09-21
  72. [72] Yres — REST service source. https://yres.eu/en/wiki/integraties/bronnen/restservice · checked 2026-09-21
  73. [73] Yres — REST: JSON interpretation. https://yres.eu/en/wiki/integraties/bronnen/restservice-json · checked 2026-09-21
  74. [74] Yres — OData source. https://yres.eu/en/wiki/integraties/bronnen/odata · checked 2026-09-21
  75. [75] Yres — OData with OAuth. https://yres.eu/en/wiki/integraties/bronnen/odata-oauth · checked 2026-09-21
  76. [76] Yres — Azure Blob Storage source (file formats). https://yres.eu/en/wiki/integraties/bronnen/azure-blob-storage · checked 2026-09-21
  77. [77] Yres — File server source. https://yres.eu/en/wiki/integraties/bronnen/file-server · checked 2026-09-21
  78. [78] Yres — SharePoint source. https://yres.eu/en/wiki/integraties/bronnen/sharepoint · checked 2026-09-21
  79. [79] Yres — Integration catalogue (all sources). https://yres.eu/en/wiki/integraties/catalogus · checked 2026-09-21
  80. [80] Yres — SAP S/4HANA. https://yres.eu/en/wiki/integraties/bronnen/sap-s4hana · checked 2026-09-21
  81. [81] Yres — SAP HANA. https://yres.eu/en/wiki/integraties/bronnen/sap-hana · checked 2026-09-21
  82. [82] Yres — SAP Analytics Cloud. https://yres.eu/en/wiki/integraties/bronnen/sac · checked 2026-09-21
  83. [83] Yres — SAP Datasphere. https://yres.eu/en/wiki/integraties/bronnen/sap-datasphere · checked 2026-09-21
  84. [84] Yres — Microsoft Graph. https://yres.eu/en/wiki/integraties/bronnen/microsoft-graph · checked 2026-09-21
  85. [85] Yres — Dynamics 365. https://yres.eu/en/wiki/integraties/bronnen/dynamics-365 · checked 2026-09-21
  86. [86] Yres — Oracle. https://yres.eu/en/wiki/integraties/bronnen/oracle · checked 2026-09-21
  87. [87] Yres — AFAS. https://yres.eu/en/wiki/integraties/bronnen/afas · checked 2026-09-21
  88. [88] Yres — Exact Online. https://yres.eu/en/wiki/integraties/bronnen/exact-online · checked 2026-09-21
  89. [89] Yres — Data sources screen (type mapping, metadata compare). https://yres.eu/en/wiki/frontend/data-sources · checked 2026-09-21
  90. [90] Yres — Load types. https://yres.eu/en/wiki/concepten/load-types · checked 2026-09-21
  91. [91] Yres — History (SCD2). https://yres.eu/en/wiki/concepten/historie-scd2 · checked 2026-09-21
  92. [92] Yres — Data flow: STAGE, HIS and views. https://yres.eu/en/wiki/concepten/gegevensstroom · checked 2026-09-21
  93. [93] Yres — SQL interaction. https://yres.eu/en/wiki/referentie/sql-interaction · checked 2026-09-21
  94. [94] Yres — Data engineering (views, persisted views, compare). https://yres.eu/en/wiki/frontend/data-engineering · checked 2026-09-21
  95. [95] Yres — Features (lineage and impact analysis). https://yres.eu/en/wiki/product/features · checked 2026-09-21
  96. [96] Yres — Lake feed (Parquet change feed). https://yres.eu/en/wiki/concepten/lake-feed · checked 2026-09-21
  97. [97] Yres — CI/CD and DTAP. https://yres.eu/en/wiki/architectuur/cicd-dtap · checked 2026-09-21
  98. [98] Yres — Change process. https://yres.eu/en/wiki/concepten/wijzigingsproces · checked 2026-09-21
  99. [99] Yres — Admin (health checks, settings, environments). https://yres.eu/en/wiki/frontend/admin · checked 2026-09-21
  100. [100] Yres — Monitoring and logging. https://yres.eu/en/wiki/referentie/monitoring-logging · checked 2026-09-21
  101. [101] Yres — Load management (master pipelines, monitoring). https://yres.eu/en/wiki/frontend/load-management · checked 2026-09-21
  102. [102] Yres — Maintenance master pipeline. https://yres.eu/en/wiki/setup/onderhouds-master-pipeline · checked 2026-09-21
  103. [103] Yres — Azure architecture. https://yres.eu/en/wiki/referentie/azure-architectuur · checked 2026-09-21
  104. [104] Yres — Archiving. https://yres.eu/en/wiki/concepten/archivering · checked 2026-09-21
  105. [105] Yres — FAQ (security, certification). https://yres.eu/en/wiki/faq · checked 2026-09-21
  106. [106] Yres — Pricing and packages. https://yres.eu/en/wiki/prijzen · checked 2026-09-21
  107. [107] Yres — Installation. https://yres.eu/en/wiki/setup/installatie · checked 2026-09-21
  108. [108] Yres — Knowledge base. https://yres.eu/en/wiki/ · checked 2026-09-21
  109. [109] Yres — Yres Academy. https://yres.eu/academy/en · checked 2026-09-21

More background: what is data warehouse automation?

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