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This episode of Techsplainers explores data streaming platforms and how they help organizations continuously capture, process, analyze and deliver data in real time or near-real time. The episode explains why these platforms have become essential as businesses shift from slower batch-based workflows to real-time decision-making powered by live data from applications, databases, sensors, logs and digital interactions.
Listeners are guided through the business context behind this shift, including the rise of big data, event-driven architectures and the need for faster analytics, AI responsiveness and operational agility. The episode also examines how data streaming platforms support AI use cases such as fraud detection, recommendation engines, predictive maintenance and AI agents that depend on current context.
From there, the discussion breaks down the four core architectural layers of a data streaming platform—source and ingestion, processing, destination and serving, and governance and management—while highlighting key characteristics such as scalability, fault tolerance, low latency and high throughput. The episode also reviews major open-source and managed technologies, including Apache Kafka, Apache Flink, Apache Spark, Confluent, Amazon Kinesis, Google Cloud Dataflow and Azure Stream Analytics.
Find more information at https://www.ibm.com/think/topics/data-streaming-platform
Narrated by Ian Smalley
Englanti
Yhdysvallat
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