The appeal of processing data in real-time is on the rise. Historically, organizations adopting the streaming data paradigm were driven by use cases such as application monitoring, log aggregation and data transformation (ETL).

Organizations like Netflix have been early adopters of the streaming data paradigm. Today, there are more drivers to growing adoption. In Lightbend’s 2019 survey, Streaming Data and the Future Tech Stack, new capabilities in artificial intelligence (AI) and machine learning (ML), integration of multiple data streams and analytics are starting to rival these historical use cases.

The streaming analytics market (which depending on definitions, may just be one segment of the streaming data market) is projected to grow from $15.4 billion in 2021 to $50.1 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 26.5% during the forecast period as per Markets and Markets.

Again, historically, there has been a sort of de-facto standard for streaming data: Apache Kafka. Kafka and Confluent, the company that commercializes it, are an ongoing success story, with Confluent confidentially filing for IPO in 2021.

In 2018, more than 90% of respondents to a Confluent survey deemed Kafka as mission-critical to their data infrastructure and queries on Stack Overflow grew over 50% during the year. As successful Confluent may be and as widely adopted as Kafka may be, however, the fact remains: Kafka’s foundations were laid in 2008.

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