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Databraid Docs

Braids

Braids are the core component of Databraid, representing powerful and flexible workflows that allow you to process and manipulate data using a combination of nodes and beads. They provide a visual and intuitive way to design and execute complex data processing pipelines, enabling you to transform, analyze, and generate insights from various data sources.

Braid Structure

A braid consists of a series of interconnected nodes and beads, forming a directed graph that defines the flow of data from input to output. Each node or bead performs a specific operation on the data, such as filtering, transforming, aggregating, or applying machine learning models.

The structure of a braid is highly customizable, allowing you to arrange nodes and beads in any desired configuration to achieve your specific data processing goals. You can connect nodes and beads using wires, establishing the data flow and dependencies between them.

Data Processing Modes

Braids in Databraid support different modes of data processing, catering to various use cases and requirements:

  1. Batch Processing: Braids can process data in batches, handling large volumes of information from sources like folders, files, or databases. This mode is suitable for scenarios where you have a fixed dataset and want to perform offline processing, such as data cleansing, transformation, or analysis.

  2. Real-time Processing: Braids can also handle real-time data streams, allowing you to process and respond to incoming data as it arrives. This mode is ideal for scenarios that require immediate processing and actionable insights, such as monitoring sensor data, analyzing social media feeds, or detecting anomalies in real-time.

  3. API Integration: Braids can integrate with external APIs to retrieve or send data. You can configure nodes to make API calls, enabling you to fetch data from various sources like news agencies, web scraping services, or third-party platforms. This integration allows you to enrich your data processing pipelines with external information and services.

Triggering Braids

Braids in Databraid can be triggered or executed in different ways, depending on your requirements:

  1. Webhook Triggering: Braids can be triggered by calling a webhook endpoint using one of the tokens created in Databraid. When the webhook is invoked, it can provide information as part of the request payload, allowing you to pass dynamic data into the braid. This triggering mechanism is useful for integrating Databraid with external systems or responding to specific events.

  2. Scheduled Execution: Braids can be scheduled to run at specific intervals or times using a cron-like syntax. This allows you to automate the execution of braids based on predefined schedules, ensuring that data processing tasks are performed regularly without manual intervention.

  3. Manual Triggering: Braids can also be manually triggered through the Databraid user interface or API. This option provides flexibility for ad-hoc data processing or when you need to initiate a braid execution based on specific user actions or requirements.

Braid Outputs

The output of a braid can vary depending on its configuration and the specific nodes and beads used. Braids can generate various types of outputs, such as:

  • Processed or transformed data in different formats (CSV, JSON, XML, etc.)
  • Visualizations, charts, or reports
  • Notifications or alerts based on specific conditions
  • Automated actions or integrations with external systems

Braids can also store the processed data in databases, files, or other storage systems for further analysis or consumption by downstream processes.

Braid Management

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Databraid provides a user-friendly interface for managing braids, allowing you to create, edit, and monitor your data processing workflows. The platform offers features like version control, collaboration, and access control, enabling teams to work together on braid development and maintenance.

You can also monitor the execution of braids, track their progress, and view logs or metrics to ensure smooth operation and identify any issues or bottlenecks.

With the power and flexibility of braids in Databraid, you can streamline your data processing workflows, automate complex tasks, and gain valuable insights from your data. Whether you are working with batch data, real-time streams, or integrating with external APIs, braids provide a robust and intuitive way to design and execute your data processing pipelines.

Refer to the Databraid documentation for detailed guides on creating braids, configuring nodes and beads, setting up triggering mechanisms, and managing your data processing workflows effectively.