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Pentaho 8.1 ya esta aquí, conoce las novedades!!


Pentaho 8.1 ya esta disponible (aquí para descargar de Sourceforge ) y que mejor que Pedro Alves para contarnos. Os dejamos las principales novedades y en que versión (EE o CE) están disponibles:
Cloud Google Storage (EE)

Google BigQuery – JDBC Support  (EE/CE)

Google BigQuery – Bulk Loader  (EE)


Google Drive  (EE/CE)

Analytics over BigQuery  (EE/CE, depending on the tool used)

Big Data / Adaptive Execution Layer (AEL) Improvements 

Bigger and Better (EE/CE)

Sub Transformation support (EE/CE)

Big Data formats: Added support for Orc (EE/CE)

Worker Nodes (EE)

New Streaming Datasources: MQTT, and JMS (Active MQ / IBM MQ) (EE/CE)


Safe Stop (EE/CE)

Streaming Dataservices (EE/CE)

CTools and Streaming Visualizations (EE/CE)

Time Series Visualizations (EE/CE)

Data Exploration Tool Updates (EE)


 Additional updates:

●     Salesforce connector API update (API version 41)
●     Splunk connection updated to version 7
●     Mongo version updated to 3.6.3 driver (supporting 3.4 and 3.6)
●     Cassandra version updated to support version 3.1 and Datastax 5.1
●     PDI repository browser performance updates, including lazy loading
●     Improvements on the Text and Hadoop file outputs, including limit and control file handling
●     Improved logging by removing auto-refresh from the kettle logging servlet
●     Admin can empty trash folder of other users on PUC
●     Clear button in PDI step search in spoon
●     Override JDBC driver class and URL for a connection
●     Suppressed the Pentaho ‘session expired’ pop-up on SSO scenarios, redirecting to the proper login page
●     Included the possibility to schedule generation of reports with a timestamp to avoid overwriting content

In summary (and wearing my marketing hat) with Pentaho 8.1 you can:

●      Deploy in hybrid and multi-cloud environments with comprehensive support for Google Cloud Platform, Microsoft Azure and AWS for both data integration and analytics
●      Connect, process and visualize streaming data, from MQTT, JMS, and IBM MQ message queues and gain insights from time series visualizations
●      Get better platform performance and increase user productivity with improved logging, additional lineage information, and faster repository access