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GenevaERS: A Growing Community Focused on Data Insights

By | September 29, 2021

Written by Kip Twitchell,  Chair of the GenevaERS Technical Steering Committee

GenevaERS graduates!

GenevaERS, which launched last September, has been promoted to an Open Mainframe Project Active project. This means the project is mature and has a strong governance and active community to move it forward. GenevaERS joins the ADE, COBOL Programming Course, Mentorship and Zowe.


What is GenevaERS?

GenevaERS fixes log jams in your computer processing. For example, a customer program took 25 hours to run. GenevaERS did it in less than one hour – on the same data on the same computer producing the same results.  How is that possible?
Reads from disk are 1,000 times slower than calculations.  Too many reads waste time.
For example: if you need 100 sales reports from a million sales records, a typical process will read the sales file 100 times, for a total of 100,000,000 reads.
GenevaERS produces all reports in one read of the file.  GenevaERS is 99 times more efficient.
You could fix this by rewriting your programs.  GenevaERS is free and does the programming for you.


The Mission

The mission of the GenevaERS Project is to radically improve business systems using Business Event principles while processing significant data volumes to achieve:
– Accurate, fast and transparent outputs,
– Respond quickly to changing business needs, and
– Minimize costs over time.
GenevaERS is the single-pass optimization engine for data extraction and reporting on z/OS. The project combines the processing power of GenevaERS, the reliability of the mainframe and the dynamics of an open-source community.

Getting Started

You can get started with GenevaERS’s Demo system, downloading sample scripts, executables, and a data generator to see the results on your own system.  Head to our demo wiki at  Learn more at


The Future

The project team is focused seeing potential advantages in GenevaERS’s single-pass optimization architecture when combined with Apache Spark.
Spark’s Map Phase tends to be one purpose: to repartition data in preparation for the real work of the Reduce Phase.
In the GenevaERS R&D Labs we’re exploring how the GenevaERS Extract engine’s ability to do many things in one pass of the data may enhance the efficiencies of Spark, while increasing GenevaERS’s functional capabilities with Spark’s Reduce Phase processing.
In the next year we will be exploring
  • Intersecting with Apache Spark
  • Converting some project components to Java
  • Adding a language specification
  • Expanding Usability and Documentation
  • Enhancing our mentoring ability for better access to z/OS

Learn About Scale and Reliability

Unlike many projects hosted by The Open Mainframe Project which enable build processes or enhance user interfaces, GenevaERS is at the heart of what makes the mainframe still relevant: highly reliable, highly scalable data workloads.
If you are interested in learning the mainframe, join the project and get hands on.  Join one of our team meetings ( and we’ll begin mentoring immediately.

Insights from Your Data

Data-driven insights have changed the world in the last 20 years; the value derived from financial data insights has lagged many other types of analysis.  GenevaERS is at the heart of the next evolution in what we know from what we measure as captured in our financial lives.
Join the GenevaERS project or use GenevaERS to be part of the next wave of data-driven insights. Learn more in this 1-minute overview.