# AI + BI Unified Workflow

Singdata Lakehouse innovatively provides an **AI + BI Unified Workflow** solution, seamlessly integrating traditional data processing pipelines with AI data application workflows on a single platform, achieving true data-intelligence convergence.

### Pain Points of the Traditional Separated Architecture

1.  **Fragmented Technology Stacks**
    *   BI systems: based on SQL, ETL tools, and reporting platforms.
    *   AI applications: based on Python, vector databases, and inference services.
    *   The two systems operate independently, making data sharing difficult.

2.  **Data Silo Problems**
    *   Structured data is stored in data warehouses.
    *   Unstructured data is scattered across object storage.
    *   Vector data is independently stored in dedicated vector databases.

3.  **Workflow Fragmentation**
    *   ETL processes and AI application processes are independent of each other.
    *   Data needs to be copied and transformed across multiple systems.
    *   High maintenance costs, and data consistency is hard to guarantee.

## Singdata Lakehouse Unified Workflow Solution

### Core Concepts

Through its unified data lakehouse architecture, Singdata Lakehouse supports running traditional ETL workflows and AI data application workflows on the same platform, achieving:

*   **Unified Storage**: Structured, unstructured, and vector data managed in a unified manner.
*   **Unified Compute**: SQL analytics and AI inference share computing resources.
*   **Unified Orchestration**: ETL tasks and AI applications are scheduled in a unified way.
*   **Unified Governance**: Data lineage, permissions, and quality are managed under a single governance framework.
