Standard Import Methods

A Consolidation Model requires that data be durable and auditable, as OneStream is functioning as the “book-of-record” for financial reporting. In this regard, the Stage Engine’s Standard Type Workflow is ideal in that each Import performed has its source and target records stored in Stage tables. This allows every current and historical period loaded to be audited and analyzed at the source and target record level used for loading data to the Finance Engine. The stored tables also allow detailed analysis from the Finance to the Stage using the Drill-Down feature.

Direct Import Methods

Certain Planning, Forecasting or Operational models may not require detailed audit and historical durability of data, with the resulting database overhead, that is required of the Consolidation Model. The Stage Engine’s Direct Load Type Workflow is specifically designed to support the needs of data that is more operational in nature. The Direct Load Type’s in-memory processing, and lack of storing Source and Target record detail, enhances its performance in processing compared to the Standard Type Workflow. The performance benefits of not storing source and target records is also what makes the Direct Load Type inappropriate for Consolidations, where detailed audit, history and drill-back is required.

Blend Import Methods

The Blend Type Workflow is an in-memory process and the integration method used by the BI Blend Engine, which writes to external relational database tables and not to a Finance Engine Cube.

The BI Blend Engine is a key element of Analytic Blend models as it is a read-only aggregate storage model. The purpose is to support reporting on large volumes of data that is not appropriate to store in a traditional Cube, such as transaction or operational data.

The Blend Type Import rationalizes the source data into a structure that is uniform and standardized for reporting by leveraging Cube Dimensions, deriving the metadata and aggregation points for the resulting BI Blend relational tables. This enables the transaction content stored in the relational tables to be aligned with the Finance Engine Cube data through common metadata and aggregation points for Analytic Blend Reporting. Refer to the BI Blend Design and Reference Guide for more detail.