Runtime baseline: Java 17, Apache Spark 3.5.0, and Scala binary version 2.12. Customers supply the applicable Spark/platform runtime, approved drivers, credentials, accounts, security configuration, and destination systems.
Install the Windows prerequisites.
See the product installation guide before downloading and usage guide after installation.
Install the Windows prerequisites.
See the product installation guide before downloading and usage guide after installation.
Production and marketplace status: the production files are locally built and validated production candidates, not marketplace-certified offers. Marketplace publication is not authorized,
submissionReady remains false, and external-platform validation is incomplete. Production use requires a written Yanbor LLC commercial agreement, order form, or license certificate. Read production status and fulfillment steps or the non-binding sample commercial agreement.| Platform | Package format | Intended distribution / fulfillment | Instructions to Evaluation | Download Evaluation | Instructions to Production | Download Production | Usage guide |
|---|---|---|---|---|---|---|---|
| .NET pipelines | .nupkg packages |
Private NuGet feed | Download the single customer-ready .NET evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run nuget-smoke-test\run-nuget-smoke-test.ps1. The local test restores both included NuGet packages and verifies their assemblies, embedded DataAI JARs, configuration JSON, Spark arguments, and function catalog. Use only an isolated non-production project and approved sample data. License: Evaluation license — .NET pipelines. Installation guide: See more... |
Customer-ready .NET evaluation ZIP SHA-256 checksum |
Add the Yanbor-authorized private feed to the project's NuGet sources, then run dotnet add package Yanbor.DataAI.Etl.Spark --version 1.0.0 --source <feed-name-or-url>. The runtime package is installed transitively and copies the licensed DataAI JARs into the application output. Keep feed credentials in the customer's approved credential store. License: Production license — .NET pipelines. Installation guide: See more... |
Integration package 1.0.0 Runtime package 1.0.0 Production checksums |
Use DataAI ETL from .NET |
| SSIS | Spark JARs (.zip); no SSIS MSI |
Licensed download portal / private NuGet feed | Download the customer-ready SSIS evaluation ZIP, verify its SHA-256 sidecar, extract it on the approved SSIS execution host, review LICENSE.md, and run smoke-test\run-smoke-test.ps1. Invoke the included DataAI Spark CLI from an SSIS Execute Process Task or approved Spark job interface, use isolated evaluation tables, and do not register Spark JARs in the GAC. No native SSIS component or MSI is included. License: Evaluation license — SSIS. Installation guide: See more... |
Customer-ready SSIS evaluation ZIP SHA-256 checksum |
No native SSIS component, MSI, or GAC-installed DataAI assembly is included. Run the licensed DataAI ETL Spark job in the customer's Spark environment and orchestrate it from SSIS with an Execute Process Task or the customer's approved Spark job interface; then consume the resulting governed tables with the existing SSIS database connector. License: Production license — SSIS. Installation guide: See more... |
DataAI ETL Spark 1.0.0 Production checksums |
Use DataAI ETL from SSIS |
| InterSystems IRIS | Adapter JAR + optional IPM (.zip) |
Open Exchange | Download the single customer-ready IRIS evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and use an isolated IRIS namespace. Run the local DataAI Spark smoke test, execute smoke-test\iris-live\setup-evaluation.sql, and then run the live IRIS smoke test with a customer-supplied compatible InterSystems JDBC driver. Require DATAAI_IRIS_SMOKE_TEST_PASS. License: Evaluation license — InterSystems IRIS. Installation guide: See more... |
Customer-ready IRIS evaluation ZIP SHA-256 checksum |
Obtain the authorized production package, verify its SHA-256 checksum, and publish the DataAI Spark modules plus dataai-spark-iris-1.0.0.jar to the customer's private Maven repository or Spark library location. Supply a customer-approved InterSystems JDBC driver separately and configure the IRIS JDBC URL, namespace, credentials, and output tables in the Spark application. The optional IPM bootstrap can be loaded with ZPM from its extracted ipm folder; it does not install the commercial Spark JARs. License: Production license — InterSystems IRIS. Installation guide: See more... |
IRIS adapter 1.0.0 Spark libraries 1.0.0 Production checksums |
Use DataAI ETL with IRIS |
| Oracle AIDP Spark | Spark JARs / Maven artifacts (.zip) |
Private Maven repository or signed download | Download the customer-ready Oracle AIDP evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run the local DataAI Spark smoke test. Upload the included DataAI JARs to a customer-controlled location visible to the isolated Oracle Spark job, configure them through the platform library mechanism or spark-submit --jars, and validate outputs in non-production schemas. License: Evaluation license — Oracle AIDP Spark. Installation guide: See more... |
Customer-ready Oracle AIDP evaluation ZIP SHA-256 checksum |
Install the immutable 1.0.0 DataAI modules from the authorized private Maven repository, or upload the required API, quality, core, functions, and optional CLI JARs to customer-controlled storage. Add the Maven coordinates through the platform's Spark package configuration or pass comma-separated JAR locations with spark-submit --jars. Keep Spark and Hadoop supplied by Oracle AIDP and store repository credentials in the approved secret facility. License: Production license — Oracle AIDP Spark. Installation guide: See more... |
Oracle package 1.0.0 Production checksums |
Use DataAI ETL with Oracle |
| Talend / MuleSoft | Maven/JAR bundle (.zip); no Mule connector |
Private Maven repository | Download the customer-ready Talend / MuleSoft evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run the local DataAI Spark smoke test. Install the included Maven artifacts in the customer's private evaluation repository for a Talend Spark job, or have MuleSoft orchestrate that isolated Spark job or consume its evaluation outputs through an existing connector. No dedicated MuleSoft connector is included. License: Evaluation license — Talend / MuleSoft. Installation guide: See more... |
Customer-ready Talend / MuleSoft evaluation ZIP SHA-256 checksum |
No dedicated Talend component or MuleSoft connector is included. For a Talend Spark job, add the licensed DataAI 1.0.0 Maven dependencies through Talend's approved artifact repository/module management and use the Java API in the Spark job. For MuleSoft, orchestrate a customer-run Spark job or consume its governed output tables through an existing database connector; do not copy DataAI Spark JARs directly into a non-Spark Mule runtime. License: Production license — Talend / MuleSoft. Installation guide: See more... |
Maven package 1.0.0 Production checksums |
Use with Talend / MuleSoft |
| Alteryx | .yxi + .zip |
Customer portal or Alteryx Marketplace | Download the single customer-ready Alteryx evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and install install\DataAI_ETL_Alteryx_2026_1_Evaluation.yxi in a non-production Alteryx Designer 2026.1 AMP environment. Run the separate local DataAI Spark smoke test, configure the customer-selected spark-submit runtime and evaluation tables, and run the fictional sample first. License: Evaluation license — Alteryx. Installation guide: See more... |
Customer-ready Alteryx evaluation ZIP SHA-256 checksum |
Download the direct DataAI_ETL_Alteryx_2026_1_Production_1.0.0.yxi, or extract it from the complete Alteryx production package, and verify it with the production checksum manifest. Double-click the YXI, install it for the current user or all users according to company policy, enable AMP in Alteryx Designer 2026.1, and configure the customer spark-submit executable, tables, rules, and commercial-rights confirmation. License: Production license — Alteryx. Installation guide: See more... |
Alteryx YXI 1.0.0 Complete Alteryx package Production checksums |
Use DataAI ETL from Alteryx |
| Power BI | Spark JARs (.zip); no .mez |
Customer portal or Microsoft-certified distribution | Download the customer-ready Power BI evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run the local DataAI Spark smoke test. Run the included libraries in an isolated Spark pipeline, publish approved non-production DataAI outputs, and connect Power BI through the customer's supported native Spark, Databricks, Fabric, or SQL path. No DataAI .mez connector is included or required. License: Evaluation license — Power BI. Installation guide: See more... |
Customer-ready Power BI evaluation ZIP SHA-256 checksum |
No DataAI .mez connector is included. Install DataAI ETL in the customer's Spark or Databricks pipeline, publish the approved DataAI output tables, and connect Power BI with its native Databricks, Spark, or database connector. Configure credentials, gateway, refresh, row-level security, and privacy settings in the customer's Power BI environment. License: Production license — Power BI. Installation guide: See more... |
Microsoft package 1.0.0 Spark libraries 1.0.0 Production checksums |
Use DataAI ETL with Power BI |
| Tableau | .twbx + adapter JAR (.zip) |
Tableau Exchange or customer portal | Download the single customer-ready Tableau evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and open install\DataAI_ETL_Accelerator.twbx in Tableau Desktop to inspect fictional embedded data. Run the local smoke test to verify the DataAI libraries and Tableau adapter, then connect the workbook to customer-approved non-production Spark SQL or Databricks output tables. No DataAI .taco connector is required. License: Evaluation license — Tableau. Installation guide: See more... |
Customer-ready Tableau evaluation ZIP SHA-256 checksum |
No DataAI .taco connector is required. Extract the authorized Tableau package and open accelerator/DataAI_ETL_Accelerator.twbx in Tableau Desktop. Install the optional dataai-spark-tableau-1.0.0.jar with the licensed DataAI modules in the customer's Spark application, publish the documented output tables, and replace the workbook's fictional source with the customer's native Spark SQL or Databricks connection. License: Production license — Tableau. Installation guide: See more... |
Tableau package 1.0.0 Spark libraries 1.0.0 Production checksums |
Use DataAI ETL with Tableau |
| AWS (Glue / EMR) | Spark JARs + container assets (.zip) |
AWS Marketplace or private S3 | Download the customer-ready AWS evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run the local DataAI Spark smoke test. Upload the included JARs to a private test S3 location and attach them to an isolated AWS Glue Spark, EMR, or EMR Serverless job through the platform's supported JAR mechanism. Use restricted IAM roles and non-production outputs; Lambda is not a supported Spark runtime. License: Evaluation license — AWS (Glue / EMR). Installation guide: See more... |
Customer-ready AWS evaluation ZIP SHA-256 checksum |
No Lambda layer or Python wheel is included, and Lambda is not a supported Spark runtime for this package. For AWS Glue Spark or Amazon EMR, place the licensed DataAI 1.0.0 JARs in a private S3 location or approved Maven repository and attach them to the Spark job with the platform's dependent-JAR setting or --extra-jars/--jars. Configure IAM access, networking, connectors, and output locations in the customer account. License: Production license — AWS (Glue / EMR). Installation guide: See more... |
AWS package 1.0.0 Production checksums |
Use DataAI ETL with AWS |
| Databricks | JARs + notebooks/configs (.zip) |
Databricks Marketplace or Unity Catalog volume | Download the customer-ready Databricks evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run the local DataAI Spark smoke test when compatible local Spark is available. Upload the included JARs and notebooks to a customer-controlled workspace or Unity Catalog volume, attach them to isolated evaluation compute, and run install\notebooks\00_INSTALL_AND_VERIFY.py before the quality, analytics, matrix, and BI examples. License: Evaluation license — Databricks. Installation guide: See more... |
Customer-ready Databricks evaluation ZIP SHA-256 checksum |
Extract the authorized production package and upload the immutable DataAI 1.0.0 JARs to an approved Unity Catalog volume, or use Yanbor's authenticated Maven repository. Add the JARs as compute or Databricks Job libraries, configure a standard-access-mode allowlist when required, import the supplied notebooks, and run 00_INSTALL_AND_VERIFY.py before the production workflow. No DataAI Python wheel is required. License: Production license — Databricks. Installation guide: See more... |
Databricks package 1.0.0 Production checksums |
Use DataAI ETL with Databricks |
| Google Cloud (Dataproc) | Spark JARs + container assets (.zip) |
Google Cloud Artifact Registry or GCS bucket | Download the customer-ready Google Cloud evaluation ZIP, verify its SHA-256 sidecar, extract it, review LICENSE.md, and run the local DataAI Spark smoke test. Upload the included JARs to a private Cloud Storage location and attach them to an isolated Dataproc Serverless batch or cluster job through --jars or the supported dependency configuration. Use restricted service accounts and non-production output locations. License: Evaluation license — Google Cloud (Dataproc). Installation guide: See more... |
Customer-ready Google Cloud evaluation ZIP SHA-256 checksum |
The production package targets Google Cloud Dataproc/Spark and includes no Python wheel. Upload the licensed DataAI 1.0.0 JARs to a private GCS location or approved Maven repository, then attach them to the Dataproc Spark job with --jars or the cluster's Spark dependency configuration. Configure service-account access, networking, connectors, and output locations in the customer project. License: Production license — Google Cloud (Dataproc). Installation guide: See more... |
Google package 1.0.0 Production checksums |
Use DataAI ETL with Google Cloud |