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GenevaERS is the Single Pass Optimization Engine.

GenevaERS began as a commercial package built by partners at Price Waterhouse Consulting, acquired by IBM® and renamed IBM Scalable Architecture for Financial Reporting™ or SAFR as an Open Source System . In 2020 it was open sourced under the original name. GenevaERS is one of the projects under the group of open source projects.

GenevaERS: Harnessing Economies of Scale for Enterprise Intelligence


Are you getting an accurate view of your enterprise? Operational data can be a valuable source of business intelligence, but is often overlooked.

Designing a reporting solution which can leverage this resource may be well worth the effort. GenevaERS can help.


■ Provides a business reporting solution for Z/OS, uniquely tuned for high-volume data scanning

■ Delivers a foundation for improved financial transparency and better decision-making

■ Mitigates audit and reconciliation concerns by tightly integrating reporting with the source data

■ Leverages existing investments and reduces operating costs by exploiting the IBM Z Integrated Information Processor (ZIIP) when the IBM Licensed version is used.

The traditional reporting model—adding more can give you less

Business analytics based on disparate corporate data can lead to unpredictable results. Typically, business intelligence systems are separate from online systems and use copies of the operational data, which by their nature are subsets captured at some prior point in time. These copies are sorted, summarized, filtered, and indexed to serve various reporting requirements, and then are retained to avoid the expensive reprocessing of detail transactions. As enterprises grow, merge, and expand, additional lines of business and organizations bring their own data and information needs, increasing the proliferation of redundant data. Consequently, additional processes must be set up for accounting, auditing, reconciliation, and restatement.

All of this contributes to evolving a reporting structure that is, in general, distanced from the original data source, relying on incomplete and/or untimely data, and burdened by complex reconciliation and restatement processes. In addition, with so many copies of the data, IT infrastructure teams must deal with increased levels of network traffic, server and storage resources, database administration, and workload management, as well as the risk associated with distributed privacy, security, and audit issues.

Rethinking the status quo—a new approach

What if the processing of operational data were to become dramatically less expensive than it has been historically? You might choose to structure your business intelligence environment differently. Reporting systems based directly on event- or transaction-level detail:

● Can produce timely, consistent, and transparent outputs,

● Can be highly responsive to changing business needs, and

● Can be implemented very rapidly.

Furthermore, enabling this “single view” of the data can help reduce the amount of siloed data in the enterprise and alleviate the pain of complex infrastructure and accounting practices.

GenevaERS offers just such a new approach to Business Intelligence.

Improved transparency and better decisions

GenevaERS is a solution designed to deal with the problem of efficiently reporting on large volumes of transactional data. It is based on a set of software components with previously patented technology embedded, and it may be customized for use with new or existing data extraction and reporting applications.

GenevaERS is often used when large amounts of operational data reside in the Z/OS® environment and complex reporting requirements create workloads that are difficult to manage through traditional techniques or tools.


● Runs quickly and efficiently

● Scales to deal with large volumes of detail data and table joins

● Creates multiple outputs, such as cubes, reports, and data marts

● Helps mitigate issues with reconciliation and restatement through a single view of the data

● Removes the need for additional transformations of the source data, allowing a simplified yet flexible and responsive data model

● Enables organizations to process data on the IBM Z™ platform where it resides

The GenevaERS solution helps support key corporate decision-making. It has a proven track record of addressing real business problems, such as account activity analysis, product pricing, fraud detection, and multi-dimensional sales revenue analysis. It has also been used to enhance existing investments in large, packaged enterprise applications and extend their capabilities. It accomplishes this by executing highly tuned batch reporting workloads on IBM Z/OS and IBM Z.

Building in performance from the ground up

Using detail transaction-level data as the backbone of financial reporting systems offers almost endless adaptability to changing business demands; however, the effort necessary to effectively utilize these high volumes should not be underestimated. Implementing workarounds that do not leverage economies of scale can create larger, more pervasive, and more complex problems in the long run.

Our experience dictates that scale must be built-in—it cannot be added after the fact. Converting a one-story building into a five-story building is much more expensive than building a five story building in the beginning. The same is true when working with business intelligence systems. GenevaERS applies proven methods and techniques to deal with the high-volume problems and scalability.

Coping with ever-shrinking batch windows

GenevaERS is designed to deal with batch window pain—the inability to create multiple outputs (cubes, reports, data marts, etc.) quickly—caused by high query volumes, high table lookup (join) volumes, and high data volumes. For:

● High query volumes—GenevaERS responds by:

— Processing multiple queries in a single scan, avoiding the use of indexes—a “single-pass architecture”

● High table lookup volumes—GenevaERS responds by:

— Exploiting shared in-memory tables and 64-bit memory

— Leveraging the symmetric multiprocessor (SMP) architecture of the mainframe

● High data volumes—GenevaERS responds by:

— Generating machine code, tailored to the specific problem at hand

— Highly tuning code for IBM Z, with short instruction path lengths

— Exploiting efficient I/O techniques for both disk and tape

— Processing data partitions in parallel

— Summarizing large quantities of data in memory

— Piping data from one transform to the next, reducing I/O

The GenevaERS architecture improves performance for data scanning operations, which are traditionally very expensive. When do scans make sense?

● When you need to summarize a whole table to get balances (as in financial reporting)

● When you need to scan a whole table for data mining purposes

● When you do not have the appropriate indexes on your database tables for random access

● When your data is on sequential media, such as tapes

GenevaERS can efficiently process data from sequential, VSAM, and DB2® sources.

Reduced reconciliation and restatement pain

GenevaERS can also help you avoid keeping extra copies of the data, minimizing reconciliation problems. While other reporting techniques require preprocessed files which are already summarized, filtered, and/or denormalized, GenevaERS can enable a “single version of the truth” that focuses on data in its original form.

In addition, GenevaERS can enable simpler data structures, requiring less extensive data conversion. Other reporting techniques have complex data structures that make it difficult to change the view of historical data (such as when you need to redraw roll-up structures, make retroactive adjustments, or backdate transactions). In contrast, GenevaERS can perform effective-dated lookups to allow users to view the data as of many different points in time.

Mass production In practice, GenevaERS is much like a “factory for reports.” Factories are able to manufacture products inexpensively on a per-unit basis because of the efficiency of sharing resources. Similarly, GenevaERS can achieve economies of scale by executing multiple reports simultaneously and sharing computing resources.

If you’re already running 40 queries or reports, the 41st report is just an incremental cost more because you’re already scanning the source table and you may be able to share the table join information. The cost per report typically declines rapidly as business demands inevitably grow, keeping your costs under tight control in an increasingly competitive and global marketplace.

The System z advantage

This GenevaERS solution can be made possible only with the inherent architectural strengths of Z/OS and IBM Z. The platform you know as “best of breed” for online transaction processing (OLTP) can be a solid foundation for Operational Business Intelligence in your enterprise. Many vendors cannot supply a truly efficient solution for operational business intelligence because of the performance implications—the volume of detail history can be overwhelming, and multiple requests contending for access to the data may cause bottlenecks in traditionally distributed systems. IBM Z thrives in this environment.

GenevaERS can run during prime time with minimal effect on your online systems. And it can run during off hours, generating massive amounts of standard reports efficiently within your batch window.

It requires the following hardware and software components:

● A IBM Z mainframe running a supported Z/OS release.

● A supported release of DB2 for z/OS®, or DB2 for Linux®. UNIX®, and Windows® (including Linux on System z), for use as a metadata repository and optionally as a data source

● z/OS DFSORT™ (5740-SM1) or a comparable tool

Also recommended:

● Tivoli® Workload Scheduler for z/OS or a comparable tool

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