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Understanding live data

Live data is where the real power of the Rebase Dashboard API comes to life - connecting to external APIs to get real-time energy production, consumption, prices, and weather data. This comprehensive integration enables dynamic, up-to-the-minute energy monitoring and analysis.

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Creating live datasets

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Basic live dataset

Connect to any REST API for real-time data feeds using the create_timeseries_live method. 
# Create a live dataset that fetches from an external API
live_dataset = client.datasets.create_timeseries_live(
    name="Live Energy Production",
    url="https://api.example.com/energy/production",
    params={
        "start": "{start}",  # These get replaced with actual timestamps
        "end": "{end}",
        "region": "DK1"
    },
    data_format="json",  # or "csv"
    aliases={
        "time": "timestamp",  # Map API column names to expected format
        "production_mw": "value"
    },
    headers={
        "Authorization": "Bearer your-api-key"
    },
    datetime_format="%Y-%m-%dT%H:%M",  # Optional: custom datetime format
    description="Real-time energy production data from external API",
    metadata={
        "unit": "MW",
        "region": "DK1",
        "data_source": "external_api"
    }
)
Key Features:
  • URL with params: The url is your external API endpoint, and params can use {start} and {end} placeholders that get replaced with actual timestamps when fetching data
  • Column mapping: Use aliases to map your API’s column names to the expected timestamp and value format
  • Flexible formats: Supports both JSON and CSV responses
  • Custom datetime formatting: If your API needs a specific datetime format, you can specify it with datetime_format
  • Headers: Add authentication or other headers as needed
Limitations:
  • Column mapping is limited to timestamp and value fields only
  • Start and end parameters are required when fetching data
  • Only JSON and CSV formats are supported
  • No automatic refresh: Live datasets don’t automatically refresh - data is fetched on-demand
  • No status monitoring: No built-in status tracking or error monitoring
  • No real-time updates: Data is fetched when requested, not continuously updated

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Using live datasets in components

The data gets fetched automatically when you use it in components: 
# The data gets fetched automatically when you use it in components
timeseries_component = client.components.create_timeseries(
    name="Live Energy Dashboard",
    title="Real-time Energy Production",
    datasets=[live_dataset.id]
)

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Advanced live dataset configurations

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CSV data format

For APIs that return CSV data: 
# Live dataset with CSV format
csv_live_dataset = client.datasets.create_timeseries_live(
    name="CSV Energy Data",
    url="https://api.example.com/energy/csv",
    params={
        "start": "{start}",
        "end": "{end}"
    },
    data_format="csv",
    aliases={
        "datetime": "timestamp",
        "power_output": "value"
    },
    headers={
        "Authorization": "Bearer your-api-key"
    },
    description="Energy data in CSV format"
)

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Complex parameter mapping

For APIs with complex parameter requirements: 
# Live dataset with complex parameters
complex_live_dataset = client.datasets.create_timeseries_live(
    name="Complex API Integration",
    url="https://api.energy-provider.com/v2/production",
    params={
        "start_time": "{start}",
        "end_time": "{end}",
        "region": "DK1",
        "data_type": "production",
        "resolution": "hourly",
        "include_metadata": "true"
    },
    data_format="json",
    aliases={
        "timestamp": "timestamp",
        "production_mw": "value",
        "capacity_mw": "capacity",
        "efficiency": "efficiency_pct"
    },
    headers={
        "Authorization": "Bearer your-api-key",
        "Accept": "application/json",
        "User-Agent": "RebaseEnergyAPI/1.0"
    },
    datetime_format="%Y-%m-%dT%H:%M:%SZ",
    description="Complex energy production data with metadata"
)

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Custom datetime formats

Handle various datetime formats from different APIs: 
# Live dataset with custom datetime format
custom_datetime_dataset = client.datasets.create_timeseries_live(
    name="Custom DateTime Format",
    url="https://api.example.com/energy",
    params={
        "start": "{start}",
        "end": "{end}"
    },
    data_format="json",
    aliases={
        "date_time": "timestamp",
        "power": "value"
    },
    datetime_format="%Y-%m-%d %H:%M:%S",  # Custom format
    headers={
        "Authorization": "Bearer your-api-key"
    },
    description="Data with custom datetime format"
)

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Use cases

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Energy trading

# Real-time energy trading data
trading_dataset = client.datasets.create_timeseries_live(
    name="Energy Trading Data",
    url="https://api.energy-exchange.com/prices",
    params={
        "start": "{start}",
        "end": "{end}",
        "market": "DK1"
    },
    data_format="json",
    aliases={
        "time": "timestamp",
        "price_eur_mwh": "value"
    },
    headers={
        "Authorization": "Bearer your-api-key"
    },
    description="Real-time electricity prices from European markets",
    metadata={
        "unit": "EUR/MWh",
        "market": "DK1",
        "data_source": "energy_exchange"
    }
)

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Wind farm monitoring

# Wind farm SCADA data
scada_dataset = client.datasets.create_timeseries_live(
    name="Wind Farm SCADA",
    url="https://scada.windfarm.com/api/production",
    params={
        "start": "{start}",
        "end": "{end}",
        "turbine_id": "all"
    },
    data_format="json",
    aliases={
        "timestamp": "timestamp",
        "production_mw": "value",
        "wind_speed": "wind_speed_ms",
        "status": "turbine_status"
    },
    headers={
        "Authorization": "Bearer your-api-key"
    },
    description="Real-time wind farm production data",
    metadata={
        "unit": "MW",
        "data_source": "scada_system"
    }
)

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Grid monitoring

# Grid operator data
grid_dataset = client.datasets.create_timeseries_live(
    name="Grid Load Data",
    url="https://api.grid-operator.com/load",
    params={
        "start": "{start}",
        "end": "{end}",
        "region": "DK1"
    },
    data_format="json",
    aliases={
        "time": "timestamp",
        "load_mw": "value",
        "frequency": "frequency_hz"
    },
    headers={
        "Authorization": "Bearer your-api-key"
    },
    description="Real-time grid load and frequency data",
    metadata={
        "unit": "MW",
        "region": "DK1",
        "data_source": "grid_operator"
    }
)

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Best practices

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Data quality

  • Ensure your API returns consistent data formats
  • Use appropriate datetime formats for your data source
  • Validate that your aliases correctly map to the expected column names
  • Test your live dataset connections before deploying to production

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Performance

  • Use appropriate time ranges in your params to avoid fetching too much data
  • Consider the refresh rate of your external API
  • Monitor data freshness and API response times
  • Use caching when possible to reduce API calls

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Reliability

  • Implement proper error handling for API failures
  • Use fallback data sources when possible
  • Monitor connection health and data flow
  • Set up alerts for data quality issues

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Security

  • Use secure authentication methods (Bearer tokens, API keys)
  • Store sensitive credentials securely
  • Implement proper access controls
  • Monitor for suspicious activity

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Troubleshooting

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Common issues

Data not updating: Check that your {start} and {end} placeholders are being replaced correctly and that your API is returning data for the specified time range. Authentication errors: Verify that your headers contain the correct authentication information and that your API key is valid. Format errors: Ensure that your data_format matches what your API returns and that your aliases correctly map the column names. Datetime parsing errors: Check that your datetime_format matches the format returned by your API.

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Debugging live datasets

# Check your live dataset
dataset = client.datasets.get_dataset(live_dataset.id)
print(f"Dataset name: {dataset.name}")
print(f"Created at: {dataset.created_at}")
print(f"Updated at: {dataset.updated_at}")

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Next steps

Now that you understand live data, explore advanced features:

Weather Integration

Combine live data with weather information.

Customization

Customize live data visualization and behavior.

API Reference

Explore all dataset endpoints and options.

Components

Learn how to use live data in components.