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.
The Latest
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...