Data modeling and management is the practice of structuring, organizing, and governing data so that business teams can build accurate reports and make informed decisions. Orbit Analytics provides advanced data modeling solutions that identify the objects needed from multidimensional data relationships, producing highly tuned queries without manual optimization.
Orbit connects to any database that complies with Java Database Connectivity (JDBC) standards, as well as flat files, cloud platforms, and proprietary applications. The Orbit database connector aggregates data from multiple sources into a unified layer, and its intelligent query generator includes only the tables each user needs for analysis.
Data Model Management and Security
Orbit’s semantic layer maps business terminology to underlying database structures, giving analysts a familiar vocabulary while IT retains governance control. This data model management approach ensures the availability, integrity, and security of every business intelligence application connected to Orbit.
Orbit finds the most efficient query path through large data volumes, which speeds decision-making and reduces network load. The platform uses a three-layer schema architecture:
Physical layer registers database objects such as tables, views, synonyms, and materialized views in the Orbit database catalog.
Logical layer creates Star Schema and Snowflake Schema fact and dimension objects. Analysts define relationships between these objects to support multidimensional analysis.
Presentation layer builds the reporting objects users need. This layer supports custom formulas, attribute columns, and metric columns for flexible report design.
Metadata Management in Orbit
Orbit stores metadata separately from application databases, giving teams a centralized system to search, capture, reuse, and publish key metadata objects. Users customize the following elements through a single interface :
-Dimensions
– Hierarchies
– Measures
– Performance metrics
– Key performance indicators (KPIs)
– Report layout objects and parameters
Separating metadata from the Orbit database layer simplifies governance and reduces the risk of conflicts between reporting logic and transactional data.
Online Analytical Processing (OLAP)
Orbit’s OLAP engine processes Star Schema and Snowflake queries to aggregate dimensional data across large datasets. Analysts drill down within OLAP cubes and into third-party data sources using customizable drill paths. This allows a finance team, for example, to move from a company-wide revenue summary to individual transaction records in three clicks.
R Statistics and Python Integration
Orbit embeds R Statistics and Python libraries directly within its BI server. Data teams create analytical models inside the metadata layer and build reports with advanced analytical visualizations, all without exporting data to external tools.
