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Connect to Sage 300 Data in AWS Glue Jobs Using JDBC

Connect to Sage 300 from AWS Glue jobs using the CData JDBC Driver hosted in Amazon S3.

AWS Glue is an ETL service from Amazon that allows you to easily prepare and load your data for storage and analytics. Using the PySpark module along with AWS Glue, you can create jobs that work with data over JDBC connectivity, loading the data directly into AWS data stores. In this article, we walk through uploading the CData JDBC Driver for Sage 300 into an Amazon S3 bucket and creating and running an AWS Glue job to extract Sage 300 data and store it in S3 as a CSV file.

Upload the CData JDBC Driver for Sage 300 to an Amazon S3 Bucket

In order to work with the CData JDBC Driver for Sage 300 in AWS Glue, you will need to store it (and any relevant license files) in a bucket in Amazon S3.

  1. Open the Amazon S3 Console.
  2. Select an existing bucket (or create a new one).
  3. Click Upload
  4. Select the JAR file (cdata.jdbc.sage300.jar) found in the lib directory in the installation location for the driver.

Configure the Amazon Glue Job

  1. Navigate to ETL -> Jobs from the AWS Glue Console.
  2. Click Add Job to create a new Glue job.
  3. Fill in the Job properties:
    • Name: Fill in a name for the job, for example: Sage300GlueJob.
    • IAM Role: Select (or create) an IAM role that has the AWSGlueServiceRole and AmazonS3FullAccess (because the JDBC Driver and destination are in an Amazon S3 bucket) permissions policies.
    • Type: Select "Spark."
    • This job runs: Select "A new script to be authored by you".
      Populate the script properties:
      • Script file name: A name for the script file, for example: GlueSage300JDBC
      • S3 path where the script is stored: Fill in or browse to an S3 bucket.
      • Temporary directory: Fill in or browse to an S3 bucket.
    • ETL language: Select "Python."
    • Expand Security configuration, script libraries and job parameters (optional). For Dependent jars path, fill in or browse to the S3 bucket where you loaded the JAR file. Be sure to include the name of the JAR file itself in the path, i.e.: s3://mybucket/cdata.jdbc.sage300.jar
  4. Click Next. Here you will have the option to add connection to other AWS endpoints, so if your Destination is Redshift, MySQL, etc, you can create and use connections to those data sources.
  5. Click "Save job and edit script" to create the job.
  6. In the editor that opens, write a python script for the job. You can use the sample script (see below) as an example.

Sample Glue Script

To connect to Sage 300 using the CData JDBC driver, you will need to create a JDBC URL, populating the necessary connection properties. Additionally, (unless you are using a Beta driver), you will need to set the RTK property in the JDBC URL. You can view the licensing file included in the installation for information on how to set this property.

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

  • Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
  • Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
  • Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

  • Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
  • User: Set this to the username of your account.
  • Password: Set this to the password of your account.

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Sage 300 JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.sage300.jar

Fill in the connection properties and copy the connection string to the clipboard.

To host the JDBC driver in Amazon S3, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

Below is a sample script that uses the CData JDBC driver with the PySpark and AWSGlue modules to extract Sage 300 data and write it to an S3 bucket in CSV format. Make any changes to the script you need to suit your needs and save the job.

import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.dynamicframe import DynamicFrame from awsglue.job import Job args = getResolvedOptions(sys.argv, ['JOB_NAME']) sparkContext = SparkContext() glueContext = GlueContext(sparkContext) sparkSession = glueContext.spark_session ##Use the CData JDBC driver to read Sage 300 data from the OEInvoices table into a DataFrame ##Note the populated JDBC URL and driver class name source_df = sparkSession.read.format("jdbc").option("url","jdbc:sage300:RTK=5246...;User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;").option("dbtable","OEInvoices").option("driver","cdata.jdbc.sage300.Sage300Driver").load() glueJob = Job(glueContext) glueJob.init(args['JOB_NAME'], args) ##Convert DataFrames to AWS Glue's DynamicFrames Object dynamic_dframe = DynamicFrame.fromDF(source_df, glueContext, "dynamic_df") ##Write the DynamicFrame as a file in CSV format to a folder in an S3 bucket. ##It is possible to write to any Amazon data store (SQL Server, Redshift, etc) by using any previously defined connections. retDatasink4 = glueContext.write_dynamic_frame.from_options(frame = dynamic_dframe, connection_type = "s3", connection_options = {"path": "s3://mybucket/outfiles"}, format = "csv", transformation_ctx = "datasink4") glueJob.commit()

Run the Glue Job

With the script written, we are ready to run the Glue job. Click Run Job and wait for the extract/load to complete. You can view the status of the job from the Jobs page in the AWS Glue Console. Once the Job has succeeded, you will have a csv file in your S3 bucket with data from the Sage 300 OEInvoices table.

Using the CData JDBC Driver for Sage 300 in AWS Glue, you can easily create ETL jobs for Sage 300 data, writing the data to an S3 bucket or loading it into any other AWS data store.