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.