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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


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


Configuration


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


Sample Configuration Example


Example Generated Event

Telematics Data Simulator


Industrial Data Simulator


Credit Card Simulator


Troubleshooting and Common Issues


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.