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IBM Completes Acquisition of StreamSets and webMethods

IBM has completed its acquisition of StreamSets and webMethods from Software AG after receiving all required regulatory approvals.

The acquisition brings together capabilities in integration, API management and data ingestion.

The acquisition builds on IBM's extensive software portfolio, with StreamSets adding new data ingestion capabilities to IBM's AI and data platform, and webMethods bringing Integration Platform as a Service (iPaas) capabilities to IBM's Automation solutions. IBM's clients and partners will now have access to one of the most modern and comprehensive application and data integration platforms in the industry to drive innovation and prepare business for AI.

"This is an important acquisition for IBM as we help our clients turn complexities into competitive advantage," said Dinesh Nirmal, Senior Vice President, Products, IBM Software. "StreamSets and webMethods bring new capabilities to our clients to embrace data and AI to better manage the growth and complexity of applications. We will empower integrators, developers, and line of business IT to build and manage integrations at an even greater and more impactful scale."

StreamSets adds cloud-based, real-time data ingestion capabilities for various types of data to watsonx, IBM's AI and data platform. Data ingestion helps move massive amounts of data from multiple sources to a centralized storage center where it can then be utilized by other systems/applications. When that data moves between sources and targets, streaming tools like StreamSets provide updated data in real-time to target destinations. This hybrid and multi-cloud ready product, which IBM plans to embed as a premium feature in watsonx.data, makes it easier for users to ingest, enrich, and harness the potential of streaming data enabled through features like offset handling and delivery guarantees.

StreamSets will also further extend the breadth and depth of IBM's Data Fabric and Data Integration capabilities through enabling the design of streaming data pipelines. It will complement IBM DataStage and Databand into a deeply integrated offering for data engineers, catering to multiple patterns of data integrated, infused with data observability capabilities. IBM plans to make StreamSets available across all major hyperscalers, including GCP (current) and Azure/AWS (in-progress), as well as on-premises.

webMethods helps organizations manage the tangled web of systems, applications and data silos within business environments. The webMethods Integration Platform as a Service (iPaaS) enables users to deploy and execute integrations anywhere, while still including outputs in unified integration flows. This helps global organizations meet local data sovereignty requirements while driving enterprise-wide innovation and taking advantage of centralized management.

IBM plans to extend the webMethods iPaaS to support the IBM integration products, giving current customers a path to multi-cloud hybrid integration. By supporting various patterns of integration, including applications, APIs, events, and B2B, IBM will help enable users to compose modern, unified, and seamless applications and services.

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IBM Completes Acquisition of StreamSets and webMethods

IBM has completed its acquisition of StreamSets and webMethods from Software AG after receiving all required regulatory approvals.

The acquisition brings together capabilities in integration, API management and data ingestion.

The acquisition builds on IBM's extensive software portfolio, with StreamSets adding new data ingestion capabilities to IBM's AI and data platform, and webMethods bringing Integration Platform as a Service (iPaas) capabilities to IBM's Automation solutions. IBM's clients and partners will now have access to one of the most modern and comprehensive application and data integration platforms in the industry to drive innovation and prepare business for AI.

"This is an important acquisition for IBM as we help our clients turn complexities into competitive advantage," said Dinesh Nirmal, Senior Vice President, Products, IBM Software. "StreamSets and webMethods bring new capabilities to our clients to embrace data and AI to better manage the growth and complexity of applications. We will empower integrators, developers, and line of business IT to build and manage integrations at an even greater and more impactful scale."

StreamSets adds cloud-based, real-time data ingestion capabilities for various types of data to watsonx, IBM's AI and data platform. Data ingestion helps move massive amounts of data from multiple sources to a centralized storage center where it can then be utilized by other systems/applications. When that data moves between sources and targets, streaming tools like StreamSets provide updated data in real-time to target destinations. This hybrid and multi-cloud ready product, which IBM plans to embed as a premium feature in watsonx.data, makes it easier for users to ingest, enrich, and harness the potential of streaming data enabled through features like offset handling and delivery guarantees.

StreamSets will also further extend the breadth and depth of IBM's Data Fabric and Data Integration capabilities through enabling the design of streaming data pipelines. It will complement IBM DataStage and Databand into a deeply integrated offering for data engineers, catering to multiple patterns of data integrated, infused with data observability capabilities. IBM plans to make StreamSets available across all major hyperscalers, including GCP (current) and Azure/AWS (in-progress), as well as on-premises.

webMethods helps organizations manage the tangled web of systems, applications and data silos within business environments. The webMethods Integration Platform as a Service (iPaaS) enables users to deploy and execute integrations anywhere, while still including outputs in unified integration flows. This helps global organizations meet local data sovereignty requirements while driving enterprise-wide innovation and taking advantage of centralized management.

IBM plans to extend the webMethods iPaaS to support the IBM integration products, giving current customers a path to multi-cloud hybrid integration. By supporting various patterns of integration, including applications, APIs, events, and B2B, IBM will help enable users to compose modern, unified, and seamless applications and services.

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Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...