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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.

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Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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