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Pentaho BI Suite 3.8 Improves Interactivity, Performance

Guided analysis, interactive dashboards without coding included in version 3.8.

Note: TDWI's editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Pentaho Corporation has released Pentaho BI Suite Enterprise Edition 3.8, an integrated commercial open source BI suite covering a wide range of BI needs, including ETL, reporting, analysis (OLAP), dashboards, and data mining. This release focuses on enabling business users to design rich, interactive BI solutions that seamlessly integrate all forms of information delivery.

The new functionality in Pentaho BI Suite Enterprise Edition 3.8 includes:

  • Guided analysis: Designers for reports, OLAP analysis, and dashboards now provide simple ways for end users to define links from one document to another, thus creating guided navigation paths to additional detail or related information.

  • Interactive dashboards with no coding: Building upon the guided analysis enhancements, Dashboard Designer allows designers to use content links to dynamically drive the filtering of other widgets displayed within the same dashboard.

  • Rich library of design tools: Designers can choose from a library of filter control types including buttons, radio groups, and calendar controls for adding interactivity to their dashboards

  • Data-less design: A new query mode allows users to design the layout of an Analyzer report before querying the underlying database. This can reduce database traffic and accelerate analytic report design when analyzing large amounts of data.

  • Data caching: Pentaho Reporting now includes an intelligent, in-memory caching layer that dramatically improves performance when working with large, parameterized, or paginated reports.

  • Deeper integration with Hadoop for Big Data: Pentaho Data Integration adds new, dedicated MapReduce steps to simplify the design of transformations as mappers, reducers, or combiners, making it easier to use as a visual interface for orchestration of Hadoop jobs.

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