Learn more about Flows here.

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Managing Flows with Lume

With Lume, you can build and manage multiple flows, potentially even hundreds or thousands. As an API, Lume can be embedded anywhere in your code, fitting seamlessly into your flow management architecture.

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Key Aspects: Flow Names and Tags

Flow names are crucial for tracking and reusing deployed flows. Use these names to route specific data to the appropriate flows. Additionally, you can add tags to your flows to better organize and filter them based on different criteria like environment, team, or purpose.

Example:

If you are normalizing data from multiple data feeds (Feed A, Feed B, and Feed C) into one internal schema, you could create three flows with the following names:

  • FeedA_to_internal
  • FeedB_to_internal
  • FeedC_to_internal

And add tags like:

  • environment:production
  • team:data-integration
  • type:normalization

When data comes in from Feed A, route it to the FeedA_to_internal flow.

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Best Practices for Naming and Tagging Flows

  • Organize Your Flows: Use flow names and tags to organize your flows. For example, create different flows per customer, per integration, or per customer system.
  • Code-Retrievable Names: Ensure names are retrievable via code. Sometimes, using a hash can work well if you want to embed the flows.
  • Consistent Tagging: Develop a consistent tagging strategy to make flows easily filterable and manageable at scale.

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Leveraging Existing Flows

When working with new data, you have two options:

  1. Reuse Existing Flows

    • Use when you want the same mapper on new data to guarantee consistent output
    • Create new runs of the same flow for recurring tasks, such as:
      • Weekly customer data syncs
      • Monthly report processing
      • Batch data normalization

    Example:

    If you have a flow that transforms customer data into your internal format, you can:

    1. Create the flow once with name customer_data_sync
    2. Create new runs of this flow whenever you need to process new customer data
    3. Automate weekly runs to keep customer data up to date

  2. Create New Flows

    • Use when you need different mappers for different data sets
    • Appropriate when transformation logic needs to be distinct
    • Helps maintain clear separation between different data processing needs

Learn more about working with Runs in our Understanding Runs guide.

For more detailed guidance, refer to our Getting Started documentation.