DataApp Overview

The Reasoning Flows component for orchestrating complete, data-driven applications.

🧭 Purpose

The DataApp object represents a complete data-driven application within Reasoning Flows.
It acts as the central orchestration point where related components — such as data preparation tools, notebooks, machine learning models, APIs, and visualizations — are logically grouped into a unified structure.

By creating a DataApp, teams can structure projects into cohesive, deployable units, simplifying management, navigation, and execution across the Reasoning Flows ecosystem.

🔹 Where It Fits in Reasoning Flows

Within the Reasoning Flows architecture:

1. Extract & Load → Ingests and structures data.
2. Transform & Prepare → Cleans and formats data for analytics.
3. AI & Machine Learning → Builds and trains predictive or generative models.
4. Visual Objects → Delivers insights through notebooks, dashboards, or APIs.
5. Data Models (Knowledge Nodes) → Define logical entities and metadata relationships.
6. DataApp → Brings all of these components together into a single, operational application.

Goal: The DataApp is the execution and delivery layer — turning Reasoning Flows projects into deployable, interactive applications.

⚙️ Key Features

• 🧩 Application Context
  Defines a complete data application inside Reasoning Flows, combining ETL pipelines, ML models, APIs, and visual assets.

• 🧠 Logical Grouping
  Groups related development objects into a single project scope — enabling consistent orchestration and versioning.

• ⚙️ Workflow Management
  Serves as a control hub for scheduling, dependency tracking, and execution order.

• 🚀 Deployment Integration
  Can be included in deployment processes to define the boundaries and logic of an entire application.

💡 Recommended Use Cases

• Building and deploying a data product or AI-powered service (e.g., forecasting app, semantic search API).
• Structuring multi-component solutions that involve data pipelines, model training, and inference endpoints.
• Managing application-level dependencies across diverse Reasoning Flows objects.
• Creating a logical hierarchy for reusable assets within enterprise-scale environments.

🖼️ Visual Example

Example: A DataApp connecting ETL pipelines, prepared tables, AutoML models, and API endpoints into a single deployable unit.

🧠 Best Practices

• Use DataApp as the top-level structure for projects — each major product or workflow should have its own.
• Keep all related objects (ETL, ML, visualization, API) grouped under one DataApp for traceability.
• Name DataApps clearly using business context (e.g., SalesForecasting_App, Customer360_App).
• Use Knowledge Nodes to document linked data models and dependencies.
• Integrate Notification Engines or Webhooks to automate reports, monitoring, and event triggers.
• Register the DataApp and its outcomes in the Knowledge Atlas for organization-wide visibility.

🔗 Related Documentation

• Visual Object Overview — Learn how to create notebooks and visualization layers within a DataApp.
• AI & Machine Learning Overview — Integrate trained models and inference engines into your DataApp.
• Transform & Prepare Overview — Prepare and clean data before inclusion in an application.
• Data Models (Knowledge Node) — Define logical structures and link them to your DataApp.
• Knowledge Atlas Overview — Document and connect your DataApp to organizational knowledge.