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Nastel Announces AutoPilot for Apache Kafka

Nastel Technologies announced AutoPilot for Apache Kafka, new technology for problems commonly experienced in today’s complex hybrid IBM middleware stacks (MQ / IIB / DataPower) and Apache Kafka environments.

AutoPilot provides operational and transactional monitoring for Apache Kafka, the open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java.

Kafka is a unified, high-throughput, low-latency platform for handling real-time data feeds. It utilizes a massively scalable pub/sub message queue architected as a distributed transaction log, as its storage layer. It is often used by our clients as a transport for streaming data interconnected with other messaging and processing systems.

AutoPilot for Kafka provides the forensics used to diagnose Kafka problems. Performance and availability monitoring of Kafka is accomplished via end-to-end stream monitoring and tracking of metrics from brokers, consumers, producers and Zookeeper, Kafka’s configuration service.

AutoPilot examines the metrics collected for Kafka topics, producers, consumers and brokers as well as offering deep-dive insight into the JVM, itself.

AutoPilot for Kafka provides:

- Auto discovery of end-to-end transactions spanning Kafka and other technologies such as IBM MQ

- Parsing of Kafka messages which are tokenized and utilized for analytics and transaction stitching

- SLA Monitoring, Analytics and Reporting

- Deep-dive monitoring of composite application components that include Kafka

- Proactive alerting and reduction in false alarms

AutoPilot also utilizes Kafka internally as an integration technology for data transport.

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Nastel Announces AutoPilot for Apache Kafka

Nastel Technologies announced AutoPilot for Apache Kafka, new technology for problems commonly experienced in today’s complex hybrid IBM middleware stacks (MQ / IIB / DataPower) and Apache Kafka environments.

AutoPilot provides operational and transactional monitoring for Apache Kafka, the open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java.

Kafka is a unified, high-throughput, low-latency platform for handling real-time data feeds. It utilizes a massively scalable pub/sub message queue architected as a distributed transaction log, as its storage layer. It is often used by our clients as a transport for streaming data interconnected with other messaging and processing systems.

AutoPilot for Kafka provides the forensics used to diagnose Kafka problems. Performance and availability monitoring of Kafka is accomplished via end-to-end stream monitoring and tracking of metrics from brokers, consumers, producers and Zookeeper, Kafka’s configuration service.

AutoPilot examines the metrics collected for Kafka topics, producers, consumers and brokers as well as offering deep-dive insight into the JVM, itself.

AutoPilot for Kafka provides:

- Auto discovery of end-to-end transactions spanning Kafka and other technologies such as IBM MQ

- Parsing of Kafka messages which are tokenized and utilized for analytics and transaction stitching

- SLA Monitoring, Analytics and Reporting

- Deep-dive monitoring of composite application components that include Kafka

- Proactive alerting and reduction in false alarms

AutoPilot also utilizes Kafka internally as an integration technology for data transport.

The Latest

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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