Infor Data Lake Connector for Azure Data Factory (Pipeline) Infor Data Lake Connector lets you connect to the Infor Data Lake within the Infor Data Fabric, allowing you to access and utilize your organization's comprehensive data assets. In this article you will learn how to quickly and efficiently integrate Infor Data Lake data in Azure Data Factory (Pipeline) without coding. We will use high-performance Infor Data Lake Connector to easily connect to Infor Data Lake and then access the data inside Azure Data Factory (Pipeline). Let's follow the steps below to see how we can accomplish that! Download Documentation Infor Data Lake Connector for Azure Data Factory (Pipeline) is based on ZappySys JDBC Bridge Driver which is part of ODBC PowerPack. It is a collection of high-performance ODBC drivers that enable you to integrate data in SQL Server, SSIS, a programming language, or any other ODBC-compatible application. ODBC PowerPack supports various file formats, sources and destinations, including REST/SOAP API, SFTP/FTP, storage services, and plain files, to mention a few.

Connect to Infor Data Lake in other apps Connect to Infor Data Lake in SSIS Connect to Infor Data Lake in SQL Server Connect to Infor Data Lake in Power BI Connect to Infor Data Lake in SSRS Connect to Infor Data Lake in Informatica Connect to Infor Data Lake in MS Access Connect to Infor Data Lake in MS Excel Connect to Infor Data Lake in SSAS Connect to Infor Data Lake in C# Connect to Infor Data Lake in Python Connect to Infor Data Lake in JAVA Connect to Infor Data Lake in Tableau Connect to Infor Data Lake in SAP Crystal Reports Connect to Infor Data Lake in Azure Data Factory (SSIS) Connect to Infor Data Lake in Talend Studio Connect to Infor Data Lake in UiPath Connect to Infor Data Lake in PowerShell Connect to Infor Data Lake in ODBC

Prerequisites

Before we begin, make sure you meet the following prerequisite:

• Java 8 Runtime Environment (JRE) installed. It is recommended to use these distributions:

  • Amazon Corretto 8
  • Oracle JRE 8

Download Infor Data Lake JDBC driver

To connect to Infor Data Lake in Azure Data Factory (Pipeline), you will have to download JDBC driver for it, which we will use in later steps. Let's perform these little steps right away:

1. Visit Infor Data Lake video tutorial or official website.
2. Follow the instructions there, download the JDBC driver, and save it locally, e.g. to D:\Drivers\JDBC\infor-compass-jdbc.jar.
   It is recommended to use JDBC driver compiled for Java 8, if possible.
3. Then download ION API file from Infor and place it in the same directory as the JDBC driver, e.g. D:\Drivers\JDBC\Infor Compass JDBC Driver.ionapi.
   Do not rename Infor Compass JDBC Driver.ionapi file as it will fail to load the driver. Also, JDBC driver and ION API file have to reside in the same folder for the driver to work.
4. Done! That was easy, wasn't it? Let's proceed to the next step.

Create ODBC Data Source (DSN) based on ZappySys JDBC Bridge Driver

Step-by-step instructions

To get data from Infor Data Lake using Azure Data Factory (Pipeline) we first need to create a DSN (Data Source) which will access data from Infor Data Lake. We will later be able to read data using Azure Data Factory (Pipeline). Perform these steps:

1. Download and install ODBC PowerPack.

2. Open ODBC Data Sources (x64):

   

3. Create a User data source (User DSN) based on ZappySys JDBC Bridge Driver

   ZappySys JDBC Bridge Driver

   
   • Create and use User DSN if the client application is run under a User Account. This is an ideal option in design-time, when developing a solution, e.g. in Visual Studio 2019. Use it for both type of applications - 64-bit and 32-bit.
   • Create and use System DSN if the client application is launched under a System Account, e.g. as a Windows Service. Usually, this is an ideal option to use in a production environment. Use ODBC Data Source Administrator (32-bit), instead of 64-bit version, if Windows Service is a 32-bit application.
   Azure Data Factory (Pipeline) uses a Service Account, when a solution is deployed to production environment, therefore for production environment you have to create and use a System DSN.

4. Now, we need to configure the JDBC connection in the new ODBC data source. Simply enter the Connection string, credentials, configure other settings, and then click Test Connection button to test the connection:

   InforDataLakeDSN

   jdbc:infordatalake://MY_DATALAKE_ID

   com.infor.idl.jdbc.Driver

   D:\Drivers\JDBC\infor-compass-jdbc.jar

   john.smith@my-company.com

   *********

   []

   

   Use these values when setting parameters:

   • Connection string: jdbc:infordatalake://MY_DATALAKE_ID
   • Driver class: com.infor.idl.jdbc.Driver
   • JDBC driver file(s): D:\Drivers\JDBC\infor-compass-jdbc.jar
   • User name: john.smith@my-company.com
   • User password: *********
   • Connection parameters: []

5. You should see a message saying that connection test is successful:

   

   Otherwise, if you are getting an error, check out our Community for troubleshooting tips.

6. We are at the point where we can preview a SQL query. For more SQL query examples visit JDBC Bridge documentation:

   InforDataLakeDSN

   SELECT * FROM "default"."orders"

   

   SELECT * FROM "default"."orders"

   You can also click on the <Select Table> dropdown and select a table from the list.

   The ZappySys JDBC Bridge Driver acts as a transparent intermediary, passing SQL queries directly to the Trino JDBC driver, which then handles the query execution. This means the Bridge Driver simply relays the SQL query without altering it.

   Some JDBC drivers don't support INSERT/UPDATE/DELETE statements, so you may get an error saying "action is not supported" or a similar one. Please, be aware, this is not the limitation of ZappySys JDBC Bridge Driver, but is a limitation of the specific JDBC driver you are using.

7. Click OK to finish creating the data source.

Video Tutorial

Read data in Azure Data Factory (ADF) from ODBC datasource (Infor Data Lake)

1. To start press New button:

   

2. Select "Azure, Self-Hosted" option:

   

3. Select "Self-Hosted" option:

   

4. Set a name, we will use "OnPremisesRuntime":

   

5. Download and install Microsoft Integration Runtime.

6. Launch Integration Runtime and copy/paste Authentication Key from Integration Runtime configuration in Azure Portal:

   

7. After finishing registering the Integration Runtime node, you should see a similar view:

   

8. Go back to Azure Portal and finish adding new Integration Runtime. You should see it was successfully added:

   

9. Go to Linked services section and create a new Linked service based on ODBC:

   

10. Select "ODBC" service:

    

11. Configure new ODBC service. Use the same DSN name we used in the previous step and copy it to Connection string box:

    InforDataLakeDSN

    DSN=InforDataLakeDSN

    

12. For created ODBC service create ODBC-based dataset:

    

13. Go to your pipeline and add Copy data connector into the flow. In Source section use OdbcDataset we created as a source dataset:

    

14. Then go to Sink section and select a destination/sink dataset. In this example we use precreated AzureBlobStorageDataset which saves data into an Azure Blob:

    

15. Finally, run the pipeline and see data being transferred from OdbcDataset to your destination dataset:

    

Conclusion

In this article we showed you how to connect to Infor Data Lake in Azure Data Factory (Pipeline) and integrate data without any coding, saving you time and effort. It's worth noting that ZappySys JDBC Bridge Driver allows you to connect not only to Infor Data Lake, but to any Java application that supports JDBC (just use a different JDBC driver and configure it appropriately).

We encourage you to download Infor Data Lake Connector for Azure Data Factory (Pipeline) and see how easy it is to use it for yourself or your team.

If you have any questions, feel free to contact ZappySys support team. You can also open a live chat immediately by clicking on the chat icon below.

Download Infor Data Lake Connector for Azure Data Factory (Pipeline) Documentation

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