Data Modeling & Management
Orbit doesn’t restrict your data sources, so your reports and analytics can span a variety of data architectures.
You can view information from flat files, as well as any database that complies with Java Database Connectivity (JDBC) standards.
Orbit can also mash data from multiple sources – including on-premise, cloud, and proprietary applications. When Orbit aggregates data, its intelligent query generator will include only the tables that users need for analysis.
Data Modeling and Security
Orbit’s innovative UI defines semantic layers that use data from your business functions. This advanced data modeling capability, combined with robust IT governance controls, ensures the availability, integrity, and security of your business intelligence applications.
Orbit’s data modeling capabilities find the most efficient path through massive volumes of data. This speeds decision-making while reducing strain on your network.
Orbit’s three-layer architecture to designing data models includes:
- Physical – allows the registration of database objects, such as tables, views, synonyms, and materialized views.
- Logical – allows the creation of Star Schema and Snowflake Schema fact- and dimension-based objects. You can also specify relationships between these objects.
- Presentation – allows the creation of the reporting object, which users require to build reports. This layer includes creating custom formulas, as well as defining attribute vs. metric columns.
Orbit enables users to search, capture, store, reuse, and publish key metadata objects. Through Orbit’s centralized management system, users can easily customize:
- Performance metrics
- Key performance indicators (KPIs)
- Report layout objects and parameters
Orbit allows you to store your metadata separately from your application databases, making your management easier and more efficient.
Online Analytical Processing (OLAP)
Orbit’s OLAP engine manages your complex business requirements. It handles Star Schema and Snowflake queries for the aggregation of dimensional data.
Orbit’s OLAP engine also handles drill down within the cube and into third-party data sources, along with customizable drill down options.
R Statistics and Python Integration
The R Statistics and Python libraries are embedded within Orbit’s BI server. This gives you the ability to create analytical models within the metadata layer, as well as build reports with advanced analytical visualizations.
Orbit in Action: City of St. Petersburg, Florida
The City of St. Petersburg wanted a scalable reporting and analytics solution to support the mayor’s Data Transparency initiative. The city’s existing tool was limited in its functionality. They also needed a solution that would integrate with Oracle EBS and their other in-house applications.
The City of St. Petersburg selected Orbit due to its flexible user interface and data visualization options. The city is currently deploying reports and dashboards that will be shared with residents of City of St. Petersburg through its website.