What is Event Stream Processing?

Event stream processing is the processing or analyzing of continuous streams of events. Event stream processing platforms process the inbound data while it is in flight. It performs ultra-fast, continuous computations against high-speed streaming data, and uses a continuous query engine that drives real-time alerts and actions as well as live, user-configured visualizations.

An event is defined as a change in state such as a transaction or a prospect navigating to your website. An event is essentially a data point captured in a business system. An event stream is a sequence of business events ordered by time. Customers are constantly purchasing, calling the helpdesk, or filling their carts in a steady stream of daily events in any business.

Event stream processing actively tracks and processes streams of events in an enterprise so that opportunities and risks can be proactively identified and business outcomes optimized. The traditional data processing approach of store-analyze-act introduces the fundamental challenge of decision latency. Information is often the most relevant as soon as it is captured, and event processing helps organizations process this information in a more timely manner. It helps solve numerous problems: identifying fraud as it happens, delivering a contextual offer while the customer is still in the store, or predicting disruptions to minimize delays. With the need to handle data in real time, event processing is becoming increasingly important.

Event Streaming vs Event Stream Processing vs Event Processing

Event stream processing is often confused with the term “event streaming.” However, event streaming simply refers to the process of moving event data from place to place efficiently so other systems can easily access and analyze it. Apache Kafka is a great example of an event streaming tool. Therefore, event streaming is a part of the event stream processing operation. There is also a distinction to be made between event processing and event stream processing. Event processing looks at individual events one at a time, whereas event stream processing handles many related events together. Event processing is like looking at individual drops of water. Event stream processing is like putting your finger under a running faucet to see how warm the water is.

Why Event Stream Processing?

In the emerging digital world, where billions of people, things, and devices interact in real time, organizations must create new and disruptive competitive advantages to drive revenue and efficiency. This is the new digital business.

Real-time data intelligence is one of the best ways to ensure business success. Businesses want to be able to react to crucial business moments in real time and traditional data processing is no longer viable in today’s world of real-time enabled systems. Because collecting information, storing it in a relational database or Hadoop cluster, and analyzing it daily, weekly, or at a chosen interval is too late. Businesses need to run queries on streaming data to discover meaningful events that allow the opportunity to automate decisions and actions so they can respond in real time. Businesses need real-time reactivity and even proactive approaches to remain competitive.

When we talk about an action in response to an event that could mean invoking an app or starting a process, storing data in a persistent ledger or streaming data to a dashboard. This leads to greater automation and being able to take advantage of events as they are happening. And, with machine learning as an option, you can not only identify the next best action but continually learn and improve business rules.

For today’s business information to be truly meaningful, you need to identify opportunities and threats hidden in these events by processing them in real time to derive insight and take appropriate action. To gain a competitive advantage from day-to-day business transactions, you can transform your organization into an event-enabled enterprise. Using an event stream processing application, you can identify opportunities and threats hidden in your business events and take action proactively and predictively. It provides the connectivity, scalability, and speed to extract actionable, real-time intelligence from high volumes of fast-moving data – enabling you to rapidly capture, analyze, and act on the trends, opportunities, and risks significant to your business.

How Does Event Stream Processing Work?

Event stream processing can make sense of vast amounts of data arriving at great velocity into your business to help determine what’s important so you can automate processes and respond to important events in real time. Event-processing programs aggregate information from distributed systems in real time, applying rules that reveal key patterns, relationships, or trends. With event stream processing you connect to all data sources and normalize, enrich and filter the data. You can then begin to correlate events and over time you see patterns emerge that describe events you care about.

The key to successful event stream processing is processing events in real time to identify the next best action. Learning and improving in a continuous loop.

An event stream processing platform delivers on the requirements of digital business, allowing you to:

  • Collect data from various sources
  • Understand the meaning of this data and its context
  • Identify and act on critical business moments

Capabilities of Event Stream Processing

Anticipate events before they occur

Centralized Collection: Event streams feed into an event distribution environment and are instantly analyzed and recorded (if necessary).

Noise Filtering: Adapters filter what should be processed, versus not, and can listen for messages from certain domains or channels. They may also standardize event format across the environment.

In-Memory Processing: Rather than analyzing data after it's reached the database, events are processed in real time using an in-memory data grid. Not only does this enable you to correlate relationships and detect meaningful patterns from significantly more data, you can do it faster and much more efficiently.

Extended Cache: Event history can live in memory for any length of time (critical for long-running event sequences) or be recorded as transactions in a stored database.

Act

Advanced Testing: Predefined parameters set the terms to measure the significance and meaning of events by comparing them to what's already circulating in memory and, if needed, by querying historical data sets. Supports all major comparison techniques, including if an event did not occur in an expected timeframe.

Business Rules: If a match is detected, business rules will determine if action is required (or not) and fire off appropriate responses if needed.

Composite Events: If a layered combination is what other rules are searching for, a new event can also be created and published as a message back into the event distribution environment for discovery.

Understand historical patterns

With event stream processing you can understand historical patterns. Opportunities and risks from the past are likely to repeat over the course of time (negative customer experiences, delays in fleet arrivals, fraudulent transactions). By identifying the pattern of events that cause them, you can track and predict when they’ll happen next.

Dynamic Sequences

With event stream processing, you can also monitor for unexpected patterns. Given the rate in which situations change – and the likelihood this frequency will increase as the speed of business accelerates – you can capture valuable insight into what's developing and decipher its contextual significance.

Event stream processing offers a distributed, stateful, rule-based event processing system that supports instant decision-making and instant actions. With event stream processing, you can correlate and find the important events in a deluge of data, minimize decision latency, and respond in the moment to bring a favorable business outcome. To remain competitive, businesses must consider augmenting their traditional business intelligence or big data strategy with real-time intelligence. Today, businesses need to move quickly on defined events and rapidly update processes to create revenue opportunities, cut costs, and minimize risks.