GenevaERS is the single-pass optimization engine for data extraction and reporting on z/OS. 

GenevaERS is the single-pass optimization engine for data extraction and reporting on z/OS. Originally developed by PricewaterhouseCoopers as the Geneva Enterprise Reporting System and acquired by IBM, GenevaERS offers businesses a high-level reporting solution uniquely tuned for big data scanning and improved financial transparency for better decision-making. This project combines the processing power of GenevaERS, the reliability of the mainframe and the dynamic open source community. 

 

GenevaERS assists in the full analytical data supply chain, from efficiently transforming data to updating reporting repositories to creating multiple analytical outputs in a single pass for enhanced high-level, scaled and integrated reporting. It can be an application development platform for high-volume systems for some of the largest businesses in the world.   

 

Under the Open Mainframe Project, GenevaERS will provide a business reporting solution for z/OS that is uniquely tuned for high-volume data scanning and improved financial transparency for better decision-making. The enhanced analysis with access to transactional detail will mitigate audit and reconciliation concerns by tightly integrating reporting with the source data. This is more than a simple reporting solution, this project creates an application development environment for high-scale, integrated general and sub ledgers for large organizations. 

 

GenevaERS is:

A powerful engine driving the enterprise data supply chain

Delivering actionable insights from masses of critical business data

Transforming what you thought possible in the daily business cycle

A Call for Participants

We’re accelerating GenevaERS’s adoption of open source and the transformation of the mainframe by focusing on a proof of concept: Integrate GenevaERS with Apache Spark.  You can read about it at the GenevaERS Apache Spark POC page.

Open source contributions are more than just about source code.  We need help of all kinds:

    • Data preparation. We are working to build a data set that can be used on either z/OS or open systems to provide a fair comparison for either platform, but with enough volume to test some performance. Work here includes scripting, data analysis, and cross-platform capabilities and design.
    • Spark. We want some good Spark code that uses the power of that system, and makes the POC give real-world results. Expertise needed includes writing Spark functions, data design, and tuning.
    • Java JNI or Java Native Interface is the means by which one calls other language routines under Java, and thus under Spark. Assistance can be used in helping to encapsulate the GenevaERS utility functions to perform fast I/O for our test.
    • GenevaERS. The configuration we create we hope to be able to extract as GenevaERS VDP XML, and provide it as a download for initial installation testing. A similar goal with the sample JCL that will be provided. GenevaERS expertise in these fields is needed.
    • Documentation Repository Work. Since this project is in its infancy, there will be numerous items to contribute along the lines of documentation and workflow.  

For more information, visit the GeneraERS GitHub or Community Page.

Download

GitHub

Chat

#genevaers