> ## Documentation Index > Fetch the complete documentation index at: https://docs.condense.io/llms.txt > Use this file to discover all available pages before exploring further. # Data Simulator (Input, Stream) **Connector Type:** Utility Connector **Description** Generates realistic synthetic event streams for testing, validating, and demonstrating Condense streaming pipelines. **Brief Description** The Data Simulator is a utility connector that generates synthetic streaming data and publishes it directly into Kafka topics within Condense. It enables developers, testers, and platform administrators to build, validate, and demonstrate streaming pipelines without requiring live production systems or physical devices. The connector supports multiple built-in simulation profiles representing common industry scenarios such as industrial IoT, fleet telematics, agriculture, wearable devices, and financial transactions. Users can configure the event generation frequency and publish the generated events into any Kafka topic for downstream processing, transformations, dashboards, or integrations. Unlike production input connectors, the Data Simulator produces synthetic events entirely within the Condense platform, making it ideal for development, proof-of-concepts, training, and performance testing. *** ## Connector Overview | **Feature** | **Value** | | :------------------------ | :--------------------------------------------------------- | | Connector Type | Utility | | Category | Synthetic Data Generator | | Input Source | Built-in Simulator | | Output | Kafka Topic | | Supported Simulator Types | Industrial, Soil Sensor, Telematics, Wearable, Credit Card | | Managed By | Condense | *** ## Core Functionality * Generates realistic synthetic event streams. * Supports multiple industry-specific simulation models. * Configurable event generation interval. * Publishes generated events directly into Kafka topics. * Enables testing of complete streaming pipelines without external systems. * Supports long-running continuous event generation. *** ## How It Works in Condense 1. Create a Data Simulator connector. 2. Select the required simulator profile. 3. Configure the event generation interval. 4. Select the Kafka topic where generated events will be published. 5. Deploy the connector. 6. Condense continuously generates synthetic events and publishes them to the configured topic until the connector is stopped. *** ## Supported Simulator Types | **Simulator** | **Description** | | :------------------------- | :------------------------------------------------------------------------------------------------------------ | | Industrial Data Simulator | Simulates industrial equipment telemetry including temperature, vibration, pressure, and machine status. | | Soil Sensor Data Simulator | Generates agricultural sensor data such as soil moisture, temperature, humidity, and pH. | | Telematics Data Simulator | Produces vehicle telemetry including GPS coordinates, speed, ignition status, fuel level, and engine metrics. | | Wearable Simulator | Simulates health metrics including heart rate, calories burned, activity level, and body temperature. | | Credit Card Simulator | Generates payment transaction events suitable for fraud detection, analytics, and financial testing. | *** ## Configuration | **Category** | **Field Name** | **Description** | **Required** | | :----------- | :------------------ | :----------------------------------------------- | :----------: | | General | Title | Unique name for the simulator connector | Yes | | Simulator | Data Simulator Type | Type of synthetic dataset to generate | Yes | | Simulator | Time Interval (ms) | Time between generated events (minimum 1000 ms) | Yes | | Messaging | Publish Topic | Kafka topic where generated events are published | Yes | *** ## Field-by-Field Explanation ### Title **Description** Friendly name used to identify the simulator inside Condense. **Recommendations** Choose names that clearly identify the simulator purpose. **Examples** * `Fleet_Telematics_Simulator` * `Industrial_Test_Data` * `CreditCard_Fraud_Test` *** ### Data Simulator Type **Description** Selects the dataset model used to generate synthetic events. **Available Options** * Industrial Data Simulator * Soil Sensor Data Simulator * Telematics Data Simulator * Wearable Simulator * Credit Card Simulator Each simulator generates events using schemas relevant to its domain. *** ### Time Interval (ms) **Description** Specifies the interval between generated events. **Minimum Value** 1000 milliseconds **Example Values** * 1000 * 2000 * 5000 * 10000 Lower intervals generate higher event throughput. *** ### Publish Topic **Description** Kafka topic where generated events will be published. **Examples** * `vehicle_telematics` * `industrial_metrics` * `soil_sensor_data` * `credit_card_transactions` The topic must already exist within the Condense workspace. *** ## Condense Configuration Environment | **Field** | **Description** | **Required** | **Example** | | :------------------ | :------------------------------- | :----------: | :--------------------------- | | Title | Connector identifier | Yes | Fleet\_Telematics\_Simulator | | Data Simulator Type | Synthetic dataset profile | Yes | Telematics Data Simulator | | Time Interval (ms) | Event generation frequency | Yes | 2000 | | Publish Topic | Kafka topic for generated events | Yes | vehicle\_telematics | *** ## Sample Configuration Example ```json theme={null} { "title": "Fleet_Telematics_Simulator", "dataSimulatorType": "Telematics Data Simulator", "timeIntervalMs": 2000, "publishTopic": "vehicle_telematics" } ``` *** ## Example Generated Event ### Telematics Data Simulator ```json theme={null} { "deviceId": "VH-1024", "timestamp": "2026-07-13T10:30:00Z", "latitude": 12.9716, "longitude": 77.5946, "speed": 68, "fuelLevel": 71.4, "engineTemperature": 92, "ignition": true } ``` *** ### Industrial Data Simulator ```json theme={null} { "machineId": "MCH-001", "timestamp": "2026-07-13T10:30:00Z", "temperature": 84.2, "pressure": 18.5, "vibration": 0.42, "status": "Running" } ``` *** ### Credit Card Simulator ```json theme={null} { "transactionId": "TXN-582103", "cardNumber": "**** **** **** 4821", "merchant": "Retail Store", "amount": 128.45, "currency": "USD", "timestamp": "2026-07-13T10:30:00Z" } ``` *** ## Troubleshooting and Common Issues | **Issue** | **Possible Cause** | **Resolution** | | :--------------------------- | :------------------------------------ | :------------------------------------------------------------------- | | Connector cannot be deployed | Required configuration missing | Complete all mandatory fields before deployment. | | No events generated | Time interval below supported minimum | Configure an interval of at least 1000 milliseconds. | | No messages in Kafka topic | Incorrect topic selected | Verify the topic exists and is correctly configured. | | Unexpected event format | Wrong simulator type selected | Select the simulator matching the intended testing scenario. | | Downstream pipeline errors | Schema mismatch | Verify downstream applications expect the generated event structure. | *** ## Advanced Considerations ### Event Rate Reducing the generation interval increases throughput. Ensure downstream pipelines are sized appropriately to handle the expected event volume. *** ### Pipeline Validation The Data Simulator is ideal for validating: * Stream processing logic * Data transformations * Aggregations * Alert generation * Dashboard visualizations * Output connectors without requiring production infrastructure. *** ### Performance Testing Multiple simulator connectors can run simultaneously to generate higher event volumes and stress-test Condense deployments. *** ### Topic Isolation Use dedicated Kafka topics for synthetic data to avoid mixing test events with production workloads. *** ## Best Practices 1. Use descriptive connector names for each simulator instance. 2. Publish synthetic data to dedicated development or testing topics. 3. Select simulator types that closely match the target production workload. 4. Increase event generation gradually when performing performance testing. 5. Stop simulator connectors after testing to prevent unnecessary resource consumption. 6. Combine Data Simulator with Condense transforms and output connectors to validate complete end-to-end streaming pipelines.