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Version: 0.0.38

Data Sources

The Data Sources Tab is a pivotal feature for Custom Models, enabling integration with various data sources vital for performing comprehensive analyses. Custom Models are reliant on structured and semi-structured data, bringing order to analytical operations through the semantics of these connections.

Importance of Data Sources​

Custom Models need data from Data Sources, which can be structured, semi-structured, or unstructured. The emphasis is placed on structured and semi-structured data, as these leverage the semantic layers of the sources, transforming into a form suitable for analysis. Administrators or designated users decide which specific data sources are accessible to a Custom Model.

Timeline Data Source​

Creating and configuring a Timeline Data Source is recommended. This approach provides a precise temporal mapping of data, essential for supporting analytical endeavors. Granularity within timeline dimensions plays a crucial role, and it is advisable to first link a Timeline to the Custom Model, followed by actual data sets.

Connecting Data Sources​

Once data sources are established, they can be linked to Custom Models, including relational databases, amongst others. Here's how to manage these connections:

Step-by-Step Guide​

  1. Navigate to Workspace

    • Open your Lakehousecat workspace.

  2. Select Custom Models

    • Choose the desired model from the list of custom models.

  3. Open the Data Sources Tab

    • Access the Data Sources section to configure or connect appropriate sources.

With available data sources, connect them to enhance the Custom Model's analytical capacity:

  • Single or Multiple Sources: Utilize one or more data sources to enrich the model interaction.
  • Ensure Domain Harmony: Link data sources with compatible domains, such as production or sales data, which share relevant focuses.
  • Experimentation and Exploration: Test various combinations once data is accessible to discover optimal configurations for user requirements.

Best Practices​

  • Semantic Integration: Leverage semantic attributes in structured data sources for enhanced analytical value.
  • Timeline Utilization: Incorporate timeline dimensions for seamless temporal data analysis.
  • Domain Compatibility: Regularly evaluate the compatibility of data sources to ensure cohesive analytical outputs.

Conclusion​

By deftly employing the Data Sources feature, users can unlock the full potential of Custom Models in Lakehousecat, gaining the flexibility to integrate vast and varied data types essential for meaningful analytics. This feature calls for rigorous exploration and adjustment to identify data sources that deliver actionable insights to users.