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IBM Delivers New Analytics for the Mainframe

IBM announced new capabilities for its System z mainframe. The integration of analytics with transactional data can provide businesses with real-time, actionable insights on commercial transactions as they occur to take advantage of new opportunities to increase sales and help minimize loss through fraud prevention.

The announcement combines the traditional transactional power of System z with Big Data analytic capabilities into a single, streamlined, end-to-end data system. This real-time integration of analytics and transaction processing can allow businesses to increase the value of a customer information profile with every interaction the customer makes.

IBM adds new analytics capabilities to the mainframe platform helping enable better data security and providing clients with the ability to integrate Hadoop big data. These offerings include:

- IBM InfoSphere BigInsights for Linux on System z – Combines open-source Apache Hadoop with IBM innovations to deliver enterprise grade Hadoop for System z clients;

- IBM DB2 Analytics Accelerator – Enhances data security while delivering 2000 times the response time for complex data queries.

Additionally, IBM unveiled new capabilities in Linux and the cloud for system z. These include:

- IBM Elastic Storage for Linux on System z – Extends the benefits of Elastic Storage to the Linux environment on IBM System z servers;

- IBM Cloud Manager with OpenStack for System z – Provides heterogeneous cloud management across System z, Power and x86 environments.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

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IBM Delivers New Analytics for the Mainframe

IBM announced new capabilities for its System z mainframe. The integration of analytics with transactional data can provide businesses with real-time, actionable insights on commercial transactions as they occur to take advantage of new opportunities to increase sales and help minimize loss through fraud prevention.

The announcement combines the traditional transactional power of System z with Big Data analytic capabilities into a single, streamlined, end-to-end data system. This real-time integration of analytics and transaction processing can allow businesses to increase the value of a customer information profile with every interaction the customer makes.

IBM adds new analytics capabilities to the mainframe platform helping enable better data security and providing clients with the ability to integrate Hadoop big data. These offerings include:

- IBM InfoSphere BigInsights for Linux on System z – Combines open-source Apache Hadoop with IBM innovations to deliver enterprise grade Hadoop for System z clients;

- IBM DB2 Analytics Accelerator – Enhances data security while delivering 2000 times the response time for complex data queries.

Additionally, IBM unveiled new capabilities in Linux and the cloud for system z. These include:

- IBM Elastic Storage for Linux on System z – Extends the benefits of Elastic Storage to the Linux environment on IBM System z servers;

- IBM Cloud Manager with OpenStack for System z – Provides heterogeneous cloud management across System z, Power and x86 environments.

The Latest

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...