Why Event Streaming is a Key Enabler of ML / AI Applications

Event Streaming Concisely Defined

With analytics applications of all flavors now rooted in machine learning and AI, event streaming is increasingly important. What is the most concise definition of event streaming? Real-time data streams flowing from financial transactions, for example, contain events such as bank deposits and customer orders. An event streaming platform such as Apache Pulsar provides real-time access to this live data, and this is event streaming. Analytics applications use Pulsar to capture these event streams and then discover patterns in the data which reveal actionable insights. 

A financial data stream is one of many important types of event streaming. A live highway traffic data feed to an e-hailing taxi application is another in which the speed and reliability of the underlying event streaming platform ultimately determines the success or failure of the enterprise.

Machine learning and AI components continually update their neural net models of live traffic conditions in order to make the most accurate predictions of driver availability and destination arrival estimates. The ultimate goal, of course, is to deliver the optimal user experience. 

Apache Pulsar provides the fastest and most reliable event streaming for AI analytics apps available in a Cloud-native messaging platform today.

And Cloud-native is what distinguishes Pulsar from competitors, because its architecture is free from infrastructure concerns. Apache Pulsar event streaming use-cases are diverse and now include data pipelines, microservices, and stream processing.

What makes Pulsar inherently cloud-native? Serverless architecture which liberates development from hardware issues and includes:

  • High performance server-to-server messaging
  • Support for multiple clusters in an instance
  • Geo-replication of messaging across clusters
  • Bookkeeper’s  persistent message storage feature
  • Separation of messaging and storage innovation
  • Native multi-tenancy support
  • Unlimited scalability for millions of topics

Event streaming creates the opportunity to analyze live event data and respond equally in real-time. When ML/AI methods identify an event pattern, then accurate decisions drive the app to respond meaningfully to the user.

In fact, this meaningful responsiveness with the “live feel” of AI-based apps is already critical to the success of many applications today. We are here to explore three key takeaway topics:

  • Cloud-native event streaming defined.
  • Why it’s necessary for AI adoption.
  • How Apache Pulsar is perfect for it.
Event Streaming Critical to AI Applications

Event streaming is a subtle but critically important component for companies in their plans to apply machine learning and AI in their niche. Manufacturing enterprises are rapidly discovering vast data logs on machine failures and deviations from scheduled machine maintenance. Simultaneously they are discovering that machine learning and AI-based analytics applications can use live event streaming to ingest this data in real-time and then provide accurate forecasts of such events to optimize factory performance. 

Updating neural network models in real-time is now recognized as critical to realizing the “live  feel” of the system in the user experience. In other words, models which update themselves only after user transactions are completed do not have that “live feel.” Cloud-optimized event streaming provided by Apache Pulsar is a quintessential component in making this optimized user experience a reality.

In the manufacturing domain, there are countless categories of data which call for live event streaming to AI analytics. Assembly completion times, peak and flow rates of capacity, order fulfillment and forecasting, to name a few. AI-driven analytics which leverage this data are most accurate when the data feed is live from the factory microcontrollers and provided by an event streaming platform like Apache Pulsar.

The types and quantity of data which companies have amassed and which can be mined for important business insight is limitless. Developers pipe enormous application error and performance log data which can be analysed in real-time by way of event streaming. Performance logs in the scope of ML / AI-based analytics can reveal system issues that affect customer experience and impact enterprise revenue. 

Airlines likewise store nearly infinite data containing ticket purchase patterns, passenger wait times, misdirected luggage issues, and flight cancelations. All of these data stores are potentially valuable in optimizing business performance outcomes. When the reality dawns that the most effective AI models must be updated in real-time, the next obvious step is that live event streaming must be implemented to make it possible.

Pulsar Perfect for AI App Event Streaming

Pulsar’s Cloud-native features make it perfect for streaming data to AI analytics applications. Pulsar is needed to drive the ML / AI paradigm shift in the scope of event streaming. Now that machine learning and AI applications demand vast storage capacity and the ability to access all historical event data in chronological order, Pulsar’s tiered storage design accommodates ML / AI data demands perfectly.

Pulsar stores events indefinitely, so it’s no longer necessary to code the coordination of other storage providers to overcome costly weaknesses in Kafka stream processing. With Pulsar, it is also easy to implement complex deployments like maintaining dedicated servers for specified tenants. This means that Pulsar’s design anticipates many of an AI applications’ needs and saves the development team from the necessity of custom coding. 

Pulsar features built-in tiered storage. All event logs are segmented to facilitate efficient offloading of inactive segments to cheaper storage options like S3 without custom coding and without compromising performance. Batch and stream pipelines can be merged, and cluster size does not need further disk storage management. All these features are configurable within Pulsar. For machine learning applications which demand fast access to both live and archived data, Pulsar is the perfect fit.

Event Streaming Empowers AI   

Developers and project managers responded in a Pulsar user survey that their top four most important reasons for choosing Pulsar included its architecture design, scalability, reliability, and Cloud-native features.

Pulsar’s architecture separates serving data from storing data into layers: serving data  is managed by the stateless “broker” nodes concept, and storage is managed by the innovative Apache BookKeeper concept. Bookkeeper is a scalable and durable message log storage concept which is ideally efficient for the machine learning AI paradigm we now inhabit. Pulsar’s storage tier is the only one of its kind among event streaming platforms which minimizes cost for the enormous data requirements of our machine learning AI epoch. Overall, Apache Pulsar is now the preferred Cloud-native messaging solution for AI-based analytics applications!

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