“ActivePivot™ brings the flexibility we require to the world in which we work.”
Head of IT, European Investment Bank
ActivePivot™ is a robust and powerful real-time object-based Online Analytical Processing (OLAP) tool that delivers incremental updates through its transactional engine and multi-threaded processing capabilities.
ActivePivot’s real-time push technology allows for timely decision making as it can aggregate multiple sources of data simultaneously, enabling fast navigation and slice and dice filtering. ActivePivot allows queries to run continuously and can generate real-time alerts on Key Performance Indicators (KPIs).
ActivePivot’s open architecture means it is easy to set up and is highly amenable to customisation and extension, to meet specific information needs.
ActivePivot offers maximum flexibility in:
- acquiring and integrating data from diverse sources
- defining and configuring hypercubes
- aggregating data within hypercubes
- deriving additional analytical information from hypercube aggregated data
- making hypercube information available to client applications
Object Orientated and Extendible
ActivePivot has an object-orientated software framework, implemented in Java and configured using XML files. It provides:
- an infrastructure for in-memory hypercubes
- a range of standard facilities for building OLAP solutions
- a rich set of interfaces to develop customised solutions
In-memory hypercubes have a huge speed advantage over relational database solutions (ROLAP). In addition, they are less constrained by external data formats, such as database schemas. This flexibility allows a hypercube to integrate data from different sources and formats: databases, files, message buses or bespoke feeds.
The independence of ActivePivot from its data sources also enables data enrichment: the calculation of additional object properties and aggregation of these within a hypercube. An original object and its calculated properties are treated as a unit (a projection) within a hypercube.
This enrichment can decompose a source object into subsidiary objects, allowing more in-depth analysis. For example, it could decompose a financial instrument into a series of periodic cash flows. Further calculations can also be performed after aggregation on the aggregated values providing additional metrics and integrating other real-time data sources such as market data.