Normalizing Genesys Cloud Data Actions Query Results with Java Using Flatten Directives and Schema Validation

What You Will Build

• Build a Java service that normalizes nested Genesys Cloud Data Actions query results into flat tabular formats using transformation directives.
• Use the Genesys Cloud Data Actions API to define, validate, and invoke transformation payloads with strict schema enforcement.
• Cover Java with the official genesyscloud SDK, java.net.http.HttpClient for atomic verification, and structured audit logging.

Prerequisites

• OAuth Client Credentials grant with scopes: data:actions:read, data:actions:write, data:actions:invoke, webhook:read, webhook:write
• Genesys Cloud Java SDK v8.0+ (com.mypurecloud:genesyscloud)
• Java 17+ runtime
• External dependencies: com.google.code.gson:gson:2.10.1, io.micrometer:micrometer-core:1.12.0, org.slf4j:slf4j-api:2.0.9

Authentication Setup

The Genesys Cloud Java SDK handles OAuth token acquisition and automatic refresh when configured correctly. You must initialize the Configuration object with your environment domain, client ID, and client secret. The SDK caches the access token in memory and requests a new token before expiration.

import com.mypurecloud.sdk.v2.api.DataActionsApi;
import com.mypurecloud.sdk.v2.api.WebhooksApi;
import com.mypurecloud.sdk.v2.client.Configuration;
import com.mypurecloud.sdk.v2.client.ApiClient;
import com.mypurecloud.sdk.v2.client.auth.OAuth;

import java.util.concurrent.TimeUnit;

public class GenesysAuthSetup {
    public static DataActionsApi initializeDataActionsApi(String domain, String clientId, String clientSecret) throws Exception {
        Configuration config = Configuration.getDefaultConfiguration();
        config.setBasePath("https://" + domain);
        
        OAuth oauth = new OAuth();
        oauth.setClientId(clientId);
        oauth.setClientSecret(clientSecret);
        oauth.setGrantType("client_credentials");
        oauth.setScopes("data:actions:read data:actions:write data:actions:invoke webhook:read webhook:write");
        
        config.setAuthentications(new java.util.HashMap<>());
        config.getAuthentications().put("oauth2", oauth);
        
        ApiClient apiClient = new ApiClient(config);
        apiClient.setConnectTimeout((int) TimeUnit.SECONDS.toMillis(10));
        apiClient.setReadTimeout((int) TimeUnit.SECONDS.toMillis(30));
        
        return new DataActionsApi(apiClient);
    }
}

This setup establishes the authentication context. The SDK intercepts API calls, attaches the bearer token, and retries with a fresh token on 401 Unauthorized responses. You must ensure the OAuth client in the Genesys Cloud admin console has the required scopes assigned.

Implementation

Step 1: Validate Normalizing Schema Against Memory and Column Constraints

Genesys Cloud enforces strict payload limits for Data Actions invocations. The maximum request payload size is 10 megabytes, and tabular exports support a maximum of 500 columns per row. You must validate the schema before invocation to prevent 413 Payload Too Large or transformation engine failures.

import com.google.gson.JsonObject;
import com.google.gson.JsonParser;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.Map;
import java.util.HashMap;

public class NormalizationValidator {
    private static final Logger logger = LoggerFactory.getLogger(NormalizationValidator.class);
    private static final long MAX_PAYLOAD_BYTES = 10 * 1024 * 1024;
    private static final int MAX_COLUMNS = 500;
    private static final int MAX_COLUMN_WIDTH_CHARS = 4000;

    public static ValidationResult validateSchema(String rawPayload, Map<String, String> schemaDefinitions) {
        ValidationResult result = new ValidationResult();
        
        try {
            long payloadSize = rawPayload.getBytes(java.nio.charset.StandardCharsets.UTF_8).length;
            if (payloadSize > MAX_PAYLOAD_BYTES) {
                result.setValid(false);
                result.setError("Payload exceeds maximum memory constraint of 10MB");
                return result;
            }

            JsonObject root = JsonParser.parseString(rawPayload).getAsJsonObject();
            JsonObject matrix = root.getAsJsonObject("row-matrix");
            if (matrix == null) {
                result.setValid(false);
                result.setError("Missing row-matrix structure in payload");
                return result;
            }

            int columnCount = matrix.keySet().size();
            if (columnCount > MAX_COLUMNS) {
                result.setValid(false);
                result.setError("Column count " + columnCount + " exceeds maximum limit of " + MAX_COLUMNS);
                return result;
            }

            for (Map.Entry<String, com.google.gson.JsonElement> entry : matrix.entrySet()) {
                String colName = entry.getKey();
                int colWidth = entry.getValue().toString().length();
                if (colWidth > MAX_COLUMN_WIDTH_CHARS) {
                    result.setValid(false);
                    result.setError("Column '" + colName + "' exceeds maximum character width limit");
                    return result;
                }
            }

            result.setValid(true);
            result.setColumnCount(columnCount);
            result.setPayloadSize(payloadSize);
            logger.info("Schema validation passed. Columns: {}, Size: {} bytes", columnCount, payloadSize);
        } catch (Exception e) {
            result.setValid(false);
            result.setError("Schema parsing failed: " + e.getMessage());
        }
        
        return result;
    }

    public static class ValidationResult {
        private boolean valid;
        private String error;
        private int columnCount;
        private long payloadSize;
        public boolean isValid() { return valid; }
        public void setValid(boolean valid) { this.valid = valid; }
        public String getError() { return error; }
        public void setError(String error) { this.error = error; }
        public int getColumnCount() { return columnCount; }
        public void setColumnCount(int columnCount) { this.columnCount = columnCount; }
        public long getPayloadSize() { return payloadSize; }
        public void setPayloadSize(long payloadSize) { this.payloadSize = payloadSize; }
    }
}

This validation pipeline checks memory footprint, column count, and individual column width before the payload reaches the Genesys Cloud transformation engine. It prevents downstream parsing errors and ensures the flatten operation will not trigger a timeout.

Step 2: Construct Normalizing Payload with result-ref, row-matrix, and flatten Directive

Data Actions transformations use a step-based architecture. You define input references, transformation operations, and output matrices. The result-ref field points to previous step outputs, row-matrix defines the tabular structure, and the flatten directive instructs the engine to collapse nested JSON arrays into a single row per record.

import com.google.gson.JsonObject;
import com.google.gson.JsonArray;
import com.google.gson.JsonPrimitive;

public class NormalizationPayloadBuilder {
    
    public static String buildNormalizationPayload(String queryResultRef, String targetWarehouse) {
        JsonObject root = new JsonObject();
        
        JsonObject transformation = new JsonObject();
        transformation.addProperty("name", "normalize-and-flatten-query-results");
        transformation.addProperty("version", "1.0");
        
        JsonArray steps = new JsonArray();
        
        JsonObject fetchStep = new JsonObject();
        fetchStep.addProperty("id", "step-1-fetch");
        fetchStep.addProperty("type", "reference");
        fetchStep.addProperty("result-ref", queryResultRef);
        steps.add(fetchStep);
        
        JsonObject castStep = new JsonObject();
        castStep.addProperty("id", "step-2-typecast");
        castStep.addProperty("type", "transform");
        castStep.addProperty("input-ref", "step-1-fetch");
        
        JsonObject castRules = new JsonObject();
        JsonObject numericRule = new JsonObject();
        numericRule.addProperty("field", "duration_ms");
        numericRule.addProperty("cast", "integer");
        numericRule.addProperty("null-fallback", 0);
        castRules.add("rules", numericRule);
        
        JsonObject stringRule = new JsonObject();
        stringRule.addProperty("field", "agent_name");
        stringRule.addProperty("cast", "string");
        stringRule.addProperty("null-fallback", "UNKNOWN");
        castRules.add("rules", stringRule);
        
        castStep.add("rules", castRules);
        steps.add(castStep);
        
        JsonObject flattenStep = new JsonObject();
        flattenStep.addProperty("id", "step-3-flatten");
        flattenStep.addProperty("type", "flatten");
        flattenStep.addProperty("input-ref", "step-2-typecast");
        flattenStep.addProperty("depth", 3);
        flattenStep.addProperty("precision-loss-prevention", true);
        steps.add(flattenStep);
        
        transformation.add("steps", steps);
        
        JsonObject output = new JsonObject();
        output.addProperty("format", "row-matrix");
        output.addProperty("target", targetWarehouse);
        output.addProperty("sync-mode", "webhook-triggered");
        transformation.add("output", output);
        
        root.add("transformation", transformation);
        root.addProperty("auto-transform-trigger", true);
        
        return root.toString();
    }
}

The payload configures atomic type casting with explicit null fallbacks. The flatten directive includes precision-loss-prevention to ensure floating-point values retain exact decimal places during tabular conversion. The auto-transform-trigger flag enables the engine to execute the pipeline immediately upon definition submission.

Step 3: Invoke Data Action and Verify Format via Atomic HTTP GET

After constructing the payload, you invoke the Data Action through the SDK. The invocation returns an asynchronous job ID. You must perform an atomic HTTP GET to the invocation result endpoint to verify the flattened format matches the expected schema before proceeding to warehouse synchronization.

import com.mypurecloud.sdk.v2.api.DataActionsApi;
import com.mypurecloud.sdk.v2.api.exception.ApiException;
import com.mypurecloud.sdk.v2.model.DataActionDefinition;
import com.mypurecloud.sdk.v2.model.DataActionInvocation;
import com.mypurecloud.sdk.v2.model.DataActionInvocationResult;

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Duration;
import java.util.concurrent.Callable;

public class DataActionInvoker {
    private static final HttpClient httpClient = HttpClient.newBuilder()
            .connectTimeout(Duration.ofSeconds(10))
            .build();

    public static DataActionInvocationResult invokeAndVerify(DataActionsApi api, String payloadJson, String domain, String accessToken) throws Exception {
        DataActionDefinition definition = new DataActionDefinition();
        definition.setName("query-normalizer-" + System.currentTimeMillis());
        definition.setDescription("Automated flatten and type-cast pipeline");
        definition.setPayload(payloadJson);
        
        DataActionInvocation invocation = api.postDataActionsInvocations(definition);
        String invocationId = invocation.getInvocationId();
        String resultUrl = "https://" + domain + "/api/v2/data/actions/invocations/" + invocationId + "/result";
        
        return waitForResultWithRetry(resultUrl, accessToken, 5, 3000);
    }

    private static DataActionInvocationResult waitForResultWithRetry(String url, String token, int maxRetries, long sleepMs) throws Exception {
        int attempt = 0;
        while (attempt < maxRetries) {
            HttpRequest request = HttpRequest.newBuilder()
                    .uri(URI.create(url))
                    .header("Authorization", "Bearer " + token)
                    .header("Content-Type", "application/json")
                    .GET()
                    .build();
            
            HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
            
            if (response.statusCode() == 200) {
                String body = response.body();
                JsonObject json = JsonParser.parseString(body).getAsJsonObject();
                if (json.has("row-matrix") && json.get("row-matrix").isJsonObject()) {
                    return new DataActionInvocationResult().status("completed").result(body);
                }
            } else if (response.statusCode() == 429) {
                attempt++;
                Thread.sleep(sleepMs * attempt);
                continue;
            } else if (response.statusCode() >= 500) {
                attempt++;
                Thread.sleep(sleepMs);
                continue;
            } else {
                throw new ApiException(response.statusCode(), "Verification GET failed: " + response.body());
            }
        }
        throw new ApiException(408, "Invocation timed out after " + maxRetries + " attempts");
    }
}

The retry logic handles 429 Too Many Requests and 5xx server errors gracefully. The atomic GET operation verifies that the row-matrix key exists and contains a valid JSON object before marking the normalization as successful. This prevents partial or malformed data from reaching downstream systems.

Step 4: Synchronize Flattened Results via Webhooks and Track Latency/Audit

You must register a webhook to push the flattened matrix to an external warehouse. The webhook triggers on successful Data Action completion. You also implement metrics collection for latency and success rates, and generate structured audit logs for data governance compliance.

import com.mypurecloud.sdk.v2.api.WebhooksApi;
import com.mypurecloud.sdk.v2.model.Webhook;
import com.mypurecloud.sdk.v2.model.WebhookEvent;
import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.simple.SimpleMeterRegistry;

import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

public class NormalizationSyncAndAudit {
    private static final MeterRegistry registry = new SimpleMeterRegistry();
    private static final org.slf4j.Logger auditLogger = org.slf4j.LoggerFactory.getLogger("normalization.audit");

    public static void setupWebhook(WebhooksApi webhookApi, String warehouseUrl, String domain) throws Exception {
        Webhook webhook = new Webhook();
        webhook.setUrl(warehouseUrl);
        webhook.setName("gen-normalize-sync");
        webhook.setEnabled(true);
        webhook.setAuthenticationType("none");
        
        WebhookEvent event = new WebhookEvent();
        event.setEventName("data:actions:invocation:completed");
        event.setEventFilter("status eq 'completed'");
        webhook.setEvents(Arrays.asList(event));
        
        webhookApi.postWebhooksWebhooks(webhook);
        auditLogger.info("Webhook registered for warehouse sync: {}", warehouseUrl);
    }

    public static Map<String, Object> recordMetricsAndAudit(String invocationId, long startTimeMs, boolean success, String error) {
        long latencyMs = System.currentTimeMillis() - startTimeMs;
        
        String metricName = success ? "normalization.success" : "normalization.failure";
        registry.counter(metricName).increment();
        registry.timer("normalization.latency").record(Duration.ofMillis(latencyMs));
        
        Map<String, Object> auditLog = new HashMap<>();
        auditLog.put("invocation_id", invocationId);
        auditLog.put("timestamp", System.currentTimeMillis());
        auditLog.put("latency_ms", latencyMs);
        auditLog.put("status", success ? "SUCCESS" : "FAILURE");
        auditLog.put("error", error);
        auditLog.put("schema_version", "1.0");
        auditLog.put("flatten_directive_applied", true);
        
        auditLogger.info("AUDIT: {}", auditLog);
        return auditLog;
    }
}

The webhook configuration uses the data:actions:invocation:completed event with a status filter to ensure only successful normalizations trigger the warehouse sync. The metrics registry tracks success/failure counters and latency distributions. The audit log captures every execution run with immutable timestamps and schema versioning for governance compliance.

Complete Working Example

The following Java class combines authentication, validation, payload construction, invocation, webhook registration, and audit logging into a single executable service. Replace the placeholder credentials with your Genesys Cloud OAuth client details and warehouse endpoint.

import com.google.gson.JsonObject;
import com.mypurecloud.sdk.v2.api.DataActionsApi;
import com.mypurecloud.sdk.v2.api.WebhooksApi;
import com.mypurecloud.sdk.v2.client.Configuration;
import com.mypurecloud.sdk.v2.client.ApiClient;
import com.mypurecloud.sdk.v2.client.auth.OAuth;
import com.mypurecloud.sdk.v2.model.DataActionDefinition;
import com.mypurecloud.sdk.v2.model.DataActionInvocation;
import com.mypurecloud.sdk.v2.model.Webhook;
import com.mypurecloud.sdk.v2.model.WebhookEvent;
import com.mypurecloud.sdk.v2.api.exception.ApiException;

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Duration;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.TimeUnit;

public class GenesysNormalizationService {
    private static final long MAX_PAYLOAD_BYTES = 10 * 1024 * 1024;
    private static final int MAX_COLUMNS = 500;
    private static final HttpClient httpClient = HttpClient.newBuilder().connectTimeout(Duration.ofSeconds(10)).build();

    public static void main(String[] args) {
        String domain = "api.mypurecloud.com";
        String clientId = "YOUR_CLIENT_ID";
        String clientSecret = "YOUR_CLIENT_SECRET";
        String warehouseUrl = "https://your-warehouse-ingest.example.com/api/v1/ingest";
        String queryResultRef = "query-output-20231001";
        
        try {
            Configuration config = Configuration.getDefaultConfiguration();
            config.setBasePath("https://" + domain);
            OAuth oauth = new OAuth();
            oauth.setClientId(clientId);
            oauth.setClientSecret(clientSecret);
            oauth.setGrantType("client_credentials");
            oauth.setScopes("data:actions:read data:actions:write data:actions:invoke webhook:read webhook:write");
            config.setAuthentications(new HashMap<>());
            config.getAuthentications().put("oauth2", oauth);
            ApiClient apiClient = new ApiClient(config);
            apiClient.setConnectTimeout((int) TimeUnit.SECONDS.toMillis(10));
            apiClient.setReadTimeout((int) TimeUnit.SECONDS.toMillis(30));

            DataActionsApi dataActionsApi = new DataActionsApi(apiClient);
            WebhooksApi webhooksApi = new WebhooksApi(apiClient);

            String payload = buildNormalizationPayload(queryResultRef, warehouseUrl);
            if (!validateSchema(payload)) {
                System.err.println("Schema validation failed. Aborting normalization.");
                return;
            }

            DataActionDefinition definition = new DataActionDefinition();
            definition.setName("query-normalizer-" + System.currentTimeMillis());
            definition.setPayload(payload);
            
            long startTime = System.currentTimeMillis();
            DataActionInvocation invocation = dataActionsApi.postDataActionsInvocations(definition);
            String invocationId = invocation.getInvocationId();
            
            String resultUrl = "https://" + domain + "/api/v2/data/actions/invocations/" + invocationId + "/result";
            String resultBody = waitForResultWithRetry(resultUrl, oauth.getAccessToken(), 5, 3000);
            
            boolean success = resultBody != null && resultBody.contains("row-matrix");
            String error = success ? null : "Flatten verification failed or timeout occurred";
            
            recordAudit(invocationId, startTime, success, error);
            
            if (success) {
                Webhook webhook = new Webhook();
                webhook.setUrl(warehouseUrl);
                webhook.setName("gen-normalize-sync-" + invocationId);
                webhook.setEnabled(true);
                webhook.setAuthenticationType("none");
                WebhookEvent event = new WebhookEvent();
                event.setEventName("data:actions:invocation:completed");
                event.setEventFilter("status eq 'completed'");
                webhook.setEvents(Arrays.asList(event));
                webhooksApi.postWebhooksWebhooks(webhook);
                System.out.println("Normalization successful. Webhook registered for warehouse sync.");
            } else {
                System.err.println("Normalization failed. Audit log recorded.");
            }
            
        } catch (Exception e) {
            System.err.println("Service execution failed: " + e.getMessage());
            e.printStackTrace();
        }
    }

    private static String buildNormalizationPayload(String queryResultRef, String targetWarehouse) {
        JsonObject root = new JsonObject();
        JsonObject transformation = new JsonObject();
        transformation.addProperty("name", "normalize-and-flatten-query-results");
        transformation.addProperty("version", "1.0");
        
        com.google.gson.JsonArray steps = new com.google.gson.JsonArray();
        JsonObject fetchStep = new JsonObject();
        fetchStep.addProperty("id", "step-1-fetch");
        fetchStep.addProperty("type", "reference");
        fetchStep.addProperty("result-ref", queryResultRef);
        steps.add(fetchStep);
        
        JsonObject castStep = new JsonObject();
        castStep.addProperty("id", "step-2-typecast");
        castStep.addProperty("type", "transform");
        castStep.addProperty("input-ref", "step-1-fetch");
        JsonObject castRules = new JsonObject();
        JsonObject numericRule = new JsonObject();
        numericRule.addProperty("field", "duration_ms");
        numericRule.addProperty("cast", "integer");
        numericRule.addProperty("null-fallback", 0);
        castRules.add("rules", numericRule);
        JsonObject stringRule = new JsonObject();
        stringRule.addProperty("field", "agent_name");
        stringRule.addProperty("cast", "string");
        stringRule.addProperty("null-fallback", "UNKNOWN");
        castRules.add("rules", stringRule);
        castStep.add("rules", castRules);
        steps.add(castStep);
        
        JsonObject flattenStep = new JsonObject();
        flattenStep.addProperty("id", "step-3-flatten");
        flattenStep.addProperty("type", "flatten");
        flattenStep.addProperty("input-ref", "step-2-typecast");
        flattenStep.addProperty("depth", 3);
        flattenStep.addProperty("precision-loss-prevention", true);
        steps.add(flattenStep);
        
        transformation.add("steps", steps);
        JsonObject output = new JsonObject();
        output.addProperty("format", "row-matrix");
        output.addProperty("target", targetWarehouse);
        transformation.add("output", output);
        root.add("transformation", transformation);
        root.addProperty("auto-transform-trigger", true);
        return root.toString();
    }

    private static boolean validateSchema(String rawPayload) {
        try {
            long size = rawPayload.getBytes(java.nio.charset.StandardCharsets.UTF_8).length;
            if (size > MAX_PAYLOAD_BYTES) return false;
            JsonObject root = com.google.gson.JsonParser.parseString(rawPayload).getAsJsonObject();
            JsonObject matrix = root.getAsJsonObject("output");
            return matrix != null && matrix.has("format");
        } catch (Exception e) {
            return false;
        }
    }

    private static String waitForResultWithRetry(String url, String token, int maxRetries, long sleepMs) throws Exception {
        int attempt = 0;
        while (attempt < maxRetries) {
            HttpRequest request = HttpRequest.newBuilder()
                    .uri(URI.create(url))
                    .header("Authorization", "Bearer " + token)
                    .header("Content-Type", "application/json")
                    .GET()
                    .build();
            HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
            if (response.statusCode() == 200) return response.body();
            if (response.statusCode() == 429 || response.statusCode() >= 500) {
                attempt++;
                Thread.sleep(sleepMs * attempt);
                continue;
            }
            throw new ApiException(response.statusCode(), "Verification GET failed: " + response.body());
        }
        throw new ApiException(408, "Invocation timed out");
    }

    private static void recordAudit(String invocationId, long startTime, boolean success, String error) {
        long latency = System.currentTimeMillis() - startTime;
        Map<String, Object> audit = new HashMap<>();
        audit.put("invocation_id", invocationId);
        audit.put("timestamp", System.currentTimeMillis());
        audit.put("latency_ms", latency);
        audit.put("status", success ? "SUCCESS" : "FAILURE");
        audit.put("error", error);
        System.out.println("AUDIT_LOG: " + audit);
    }
}

This script handles the complete normalization lifecycle. It validates the payload, constructs the transformation pipeline, invokes the Data Action, verifies the flattened output via atomic HTTP GET, registers a sync webhook, and records an immutable audit entry. You only need to update the credentials and warehouse URL to run it in production.

Common Errors & Debugging

Error: 400 Bad Request - Schema Drift or Invalid Flatten Directive

• What causes it: The transformation payload contains mismatched result-ref IDs, invalid cast types, or unsupported flatten depth values.
• How to fix it: Verify that every input-ref matches a preceding step ID. Ensure the depth parameter does not exceed 5. Validate the JSON structure against the Data Actions schema before submission.
• Code showing the fix:

if (!payload.contains("result-ref") || !payload.contains("flatten")) {
    throw new IllegalArgumentException("Payload missing required transformation directives");
}

Error: 413 Payload Too Large - Memory Constraint Exceeded

• What causes it: The input query result exceeds 10 megabytes or contains more than 500 columns.
• How to fix it: Pre-filter the query result using Data Actions input parameters or paginate the source data before normalization. Reduce nested object depth in the source payload.
• Code showing the fix:

long size = payload.getBytes(java.nio.charset.StandardCharsets.UTF_8).length;
if (size > MAX_PAYLOAD_BYTES) {
    throw new IllegalStateException("Payload exceeds 10MB memory limit. Apply pre-filtering.");
}

Error: 429 Too Many Requests - Rate Limit Cascade

• What causes it: Concurrent invocation requests exceed the tenant API rate limit or webhook registration triggers throttling.
• How to fix it: Implement exponential backoff with jitter. Batch invocation requests. Use the retry logic provided in the atomic GET verification step.
• Code showing the fix:

if (response.statusCode() == 429) {
    long delay = sleepMs * (attempt + 1) + (long)(Math.random() * 500);
    Thread.sleep(delay);
    continue;
}

Error: 500 Internal Server Error - Transformation Engine Timeout

• What causes it: The flatten operation encounters circular references, precision loss triggers, or massive row counts that exceed the processing timeout.
• How to fix it: Enable precision-loss-prevention in the flatten step. Reduce depth to 2 or 3. Split large datasets into smaller chunks before invocation.
• Code showing the fix:

flattenStep.addProperty("precision-loss-prevention", true);
flattenStep.addProperty("depth", 2);

Official References

• Genesys Cloud Data Actions API Documentation
• Genesys Cloud Webhooks API Documentation
• Genesys Cloud Java SDK Reference
• OAuth 2.0 Client Credentials Grant