Spark on z/OS and Jupyter: fast, flexible analysis of mainframe data

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blogzOSSparkJupyterMany enterprises are faced with the need to expand data processing access to users without impacting mission-critical transactional application environments. The trending approach to this problem is to move the data from these systems of record to a data warehouse. Moving data-at-rest to a mirrored data repository for analytics can yield costly side-effects such as expensive migration workloads, data concurrency and data security. Alternatively, the distillation of data-at-rest to isolate information of interest for downstream analytics can offer optimal results.

IBM Announces

Today, IBM announced the release of the IBM z/OS Platform for Apache Spark. This enterprise-grade, native z/OS distribution of the open source in-memory analytics engine, enables the analyzing of business-critical z/OS data sources (such as DB2, IMS, VSAM and Adabas, among others) in place — with no data movement.

Designed to simplify the analysis of big data, this no-charge product allows enterprises to ingest data from diverse sources, including systems of record on the z/OS platform, and apply fast and federated large-scale data processing analytics using consistent Apache Spark APIs. By co-locating the data with the distillation and analysis jobs, companies can improve the time to value for actionable insights in a non-intrusive and cost effective manner.

7388996GitHub Tools

In conjunction with this product announcement, IBM has created a new GitHub organization to promote the development of an ecosystem of tools around Spark on z/OS. IBM has seeded the new organization with two GitHub repositories derived from Project Jupyter, namely the Scala Workbench and Interactive Insights Workbench. These tools are centered around a reference architecture that is aimed at bootstrapping data scientists who lack access to mainframe data stores.

 

rocket-ref-arch

 

The data wrangler uses the z/OS Platform for Apache Spark to distill and analyze systems of record and then place the results into a NoSQL data repository, conforming to tidy data principles. With the complex task of munging multiple datasets already handled, data scientists can focus their energies on exploratory and predictive analytics using R or Python within a Jupyter notebook. This reference architecture offers a flexible, extensible, and interoperable solution for a variety of use cases. The open source Apache Spark distribution for z/OS, without data abstraction services, can be downloaded directly from DeveloperWorks and deployed using this installation guide.

 

Next Steps

The full package, including data abstraction, can be ordered at no-charge from Shopz.  If you’re really interested in using Spark on the mainframe but feel that you need help getting

rocket-ibm-z-logostarted, Rocket Software is offering Launchpad, an engagement model designed to help you get your project ideas into production.

 

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Dan Gisolfi
As CTO for Trusted Identity, Dan is focused on the development and execution of a trusted identity strategy for both citizen and corporate identity interactions using blockchain technologies. This endeavor includes the development of a formal IBM Mobile Identity offering, the definition and development of a trusted identity reference architecture, and the creation of devops tools that streamline the delivery of trusted identity solutions for clients.
Dan Gisolfi
Dan Gisolfi

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