What is streaming analytics?

This episode of Techsplainers explores streaming analytics, the real-time analytics layer that helps organizations continuously ingest, process and analyze streaming data as it is generated. The episode explains how streaming analytics differs from traditional batch-based approaches by delivering insights with minimal latency, enabling businesses to act on events while they are still unfolding. Listeners are guided through why streaming analytics matters in modern business environments shaped by IoT, SaaS applications, financial transactions, social media and other continuous data sources. The discussion breaks down the four major components of a streaming analytics workflow—data ingestion, data processing and analysis, governance, and consumption—while also clarifying the subtle distinction between streaming analytics and real-time analytics. The episode highlights the growing connection between streaming analytics and AI, including support for AI agents, chatbots, recommendation engines, fraud detection models and predictive maintenance systems that depend on fresh, contextualized data. It also examines key benefits such as faster decision-making, better customer experiences, improved operational efficiency and stronger security, alongside the challenges of data integration, quality, governance, scalability and low-latency delivery. Major technologies such as Apache Kafka, Apache Flink, Spark Structured Streaming, Confluent and Iceberg are also introduced. Find more information at https://www.ibm.com/think/topics/streaming-analytics Find more episodes https://www.ibm.biz/techsplainers-podcast Narrated by Ian Smalley