Read source from connector

Specific operations: Read Source from Connector

Breadcrumb:
Right click under sources into the Source repository

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Overview

The Read Source from Connector feature in AnalyticsCreator enables you to automatically read and import the metadata structure of a source system—such as SQL Server, SAP, Oracle, Snowflake, or others—into your AnalyticsCreator project. This operation pulls table definitions, column names, data types, and optionally primary key information, populating the staging area of your metadata model.

This feature is foundational for metadata-driven automation. Rather than manually defining tables, users can connect to the source system and generate table structures from live metadata, significantly accelerating project setup and ensuring accuracy in source mappings.

When and Why to Use This Feature

• At the beginning of a new project to import the source schema automatically
• When the source system structure changes and needs to be synchronized with the model
• To ensure consistency between the staging layer and the live source
• To support data lineage, auditing, and governance by maintaining traceability of source metadata

How to Use Read Source from Connector

1. In the main toolbar, click Specific Operations.
2. Select Read Source from Connector.
3. Choose the data source connection already configured in the project (e.g., SQL Server, SAP, Oracle).
4. Select one or more schemas and tables you want to import.
5. Click Read to load the metadata into the staging area of your model.
6. AnalyticsCreator creates metadata objects for each selected table, including column types and technical attributes.

What This Operation Imports

• Tables: Names and schema locations
• Columns: Data types, lengths, and nullability
• Keys (if available): Primary and foreign keys where supported by the connector
• Technical metadata: Source system ID, table description, source path

How It Works in AnalyticsCreator

The Read Source from Connector function is powered by the connector engine within AnalyticsCreator, which is designed to integrate with a wide range of enterprise data platforms. The imported metadata is stored in the model repository and immediately available in the Staging Area screen, where you can begin transformations, data quality rules, or mapping to data marts.

Because this metadata is fully integrated with the AnalyticsCreator model, it supports downstream automation such as:

• Auto-generating ADF pipelines for ingestion
• Staging layer deployment to Fabric SQL or Azure Synapse
• Semantic layer integration with Power BI or SSAS
• Change tracking and source-to-target traceability

Benefits of Read Source from Connector