Uso de CreateMatchingWorkflow con un AWS SDK

    /**
     * Creates an asynchronous CompletableFuture to manage the creation of a matching workflow.
     *
     * @param roleARN                 the AWS IAM role ARN to be used for the workflow execution
     * @param workflowName            the name of the workflow to be created
     * @param outputBucket            the S3 bucket path where the workflow output will be stored
     * @param jsonGlueTableArn        the ARN of the Glue Data Catalog table to be used as the input source
     * @param jsonErSchemaMappingName the name of the schema to be used for the input source
     * @return a CompletableFuture that, when completed, will return the ARN of the created workflow
     */
    public CompletableFuture<String> createMatchingWorkflowAsync(
        String roleARN
        , String workflowName
        , String outputBucket
        , String jsonGlueTableArn
        , String jsonErSchemaMappingName
        , String csvGlueTableArn
        , String csvErSchemaMappingName) {

        InputSource jsonInputSource = InputSource.builder()
            .inputSourceARN(jsonGlueTableArn)
            .schemaName(jsonErSchemaMappingName)
            .applyNormalization(false)
            .build();

        InputSource csvInputSource = InputSource.builder()
            .inputSourceARN(csvGlueTableArn)
            .schemaName(csvErSchemaMappingName)
            .applyNormalization(false)
            .build();

        OutputAttribute idOutputAttribute = OutputAttribute.builder()
            .name("id")
            .build();

        OutputAttribute nameOutputAttribute = OutputAttribute.builder()
            .name("name")
            .build();

        OutputAttribute emailOutputAttribute = OutputAttribute.builder()
            .name("email")
            .build();

        OutputAttribute phoneOutputAttribute = OutputAttribute.builder()
            .name("phone")
            .build();

        OutputSource outputSource = OutputSource.builder()
            .outputS3Path("s3://" + outputBucket + "/eroutput")
            .output(idOutputAttribute, nameOutputAttribute, emailOutputAttribute, phoneOutputAttribute)
            .applyNormalization(false)
            .build();

        ResolutionTechniques resolutionType = ResolutionTechniques.builder()
            .resolutionType(ResolutionType.ML_MATCHING)
            .build();

        CreateMatchingWorkflowRequest workflowRequest = CreateMatchingWorkflowRequest.builder()
            .roleArn(roleARN)
            .description("Created by using the AWS SDK for Java")
            .workflowName(workflowName)
            .inputSourceConfig(List.of(jsonInputSource, csvInputSource))
            .outputSourceConfig(List.of(outputSource))
            .resolutionTechniques(resolutionType)
            .build();

        return getResolutionAsyncClient().createMatchingWorkflow(workflowRequest)
            .whenComplete((response, exception) -> {
                if (response != null) {
                    logger.info("Workflow created successfully.");
                } else {
                    Throwable cause = exception.getCause();
                    if (cause instanceof ValidationException) {
                        throw new CompletionException("Invalid request: Please check input parameters.", cause);
                    }

                    if (cause instanceof ConflictException) {
                        throw new CompletionException("A conflicting workflow already exists. Resolve conflicts before proceeding.", cause);
                    }
                    throw new CompletionException("Failed to create workflow: " + exception.getMessage(), exception);
                }
            })
            .thenApply(CreateMatchingWorkflowResponse::workflowArn);
    }

//The default inputs for this demo are read from the ../inputs.json.

import { fileURLToPath } from "node:url";

import {
  CreateMatchingWorkflowCommand,
  EntityResolutionClient,
} from "@aws-sdk/client-entityresolution";
import data from "../inputs.json" with { type: "json" };

const region = "eu-west-1";
const erClient = new EntityResolutionClient({ region: region });

export const main = async () => {
  const createMatchingWorkflowParams = {
    roleArn: `${data.inputs.roleArn}`,
    workflowName: `${data.inputs.workflowName}`,
    description: "Created by using the AWS SDK for JavaScript (v3).",
    inputSourceConfig: [
      {
        inputSourceARN: `${data.inputs.JSONinputSourceARN}`,
        schemaName: `${data.inputs.schemaNameJson}`,
        applyNormalization: false,
      },
      {
        inputSourceARN: `${data.inputs.CSVinputSourceARN}`,
        schemaName: `${data.inputs.schemaNameCSV}`,
        applyNormalization: false,
      },
    ],
    outputSourceConfig: [
      {
        outputS3Path: `s3://${data.inputs.myBucketName}/eroutput`,
        output: [
          {
            name: "id",
          },
          {
            name: "name",
          },
          {
            name: "email",
          },
          {
            name: "phone",
          },
        ],
        applyNormalization: false,
      },
    ],
    resolutionTechniques: { resolutionType: "ML_MATCHING" },
  };
  try {
    const command = new CreateMatchingWorkflowCommand(
      createMatchingWorkflowParams,
    );
    const response = await erClient.send(command);

    console.log(
      `Workflow created successfully.\n The workflow ARN is: ${response.workflowArn}`,
    );
  } catch (caught) {
    console.error(caught.message);
    throw caught;
  }
};