Event stream processing (ESP) is a technology that can process a continuous flow of data as soon as an event or change happens. By processing single points of data rather than an entire batch, event streaming platforms provide an architecture that enable software to understand, react to, and operate as events occur.
*Learn how event stream processing works, its major benefits, and how to get started building event-driven architectures in the free stream processing guide. *
Whether in e-commerce, finance, travel, or gaming, every business is inundated with event streams on a day-to-day basis. With customers increasingly looking for responsive interactions and experiences, companies are just discovering the importance of event streaming, allowing real-time data to be processed, stored, and acted upon as real-time events occur. Learn how event streaming is revolutionizing the way business run with an overview of how event streams work, benefits, and use cases.
Similar to streaming data, event sourcing, complex event processing (CEP), event streaming is the continuous flow of data generated with each event, or change of state.
By using event stream processing technologies like Apache Kafka, these events (i.e. a credit card swype, server outage, or social media update) can be processed, stored, analyzed, and acted upon as they're generated in real-time.
Data processing is not new. In previous years, legacy infrastructure was much more structured because it only had a handful of sources that generated data and the entire system could be architected in a way to specify and unify the data and data structures.
Modern data is generated by an infinite amount of sources whether it’s from hardware sensors, servers, mobile devices, applications, web browsers, internal and external and it’s almost impossible to regulate or enforce the data structure or control the volume and frequency of the data generated.
Applications that analyze and process data streams need to process one data packet at a time, in sequential order. Each data packet generated will include the source and timestamp to enable applications to work with data streams.
Applications working with data streams will always require two main functions: storage and processing. Storage must be able to record large streams of data in a way that is sequential and consistent. Processing must be able to interact with storage, consume, analyze and run computation on the data.
This also brings up additional challenges and considerations when working with data streams. Many platforms and tools are now available to help companies build streaming data applications.
Legacy batch data processing methods required data to be collected in batch form before it could be processed, stored, or analyzed whereas streaming data flows in continuously, allowing that data to be processed in real time without waiting for it to arrive in batch form.
Today, data arrives naturally as never ending streams of events. This data comes in all volumes, formats, from various locations and cloud, on-premises, or hybrid cloud.
With the complexity of today's modern requirements, legacy data processing methods have become obsolete for most use cases, as it can only process data as groups of transactions collected over time. Modern organizations actively use real-time data streams, acting on up-to-the-millisecond data. This continuous data offers numerous advantages that are transforming the way businesses run.
Some common examples of streaming data are real-time stock trades, retail inventory management, and ride-sharing apps.
For example, when a passenger calls Lyft, not only does the application know which driver to match them to, they know how long it will take based on real-time location data and historical traffic data, and how much it should cost based on both real-time and past data.
Data streams play a key part in the world of big data, providing real-time analyses, data integration, and data ingestion.
Used by 80% of the Fortune 100, Confluent's data streaming platfoirm helps you set your data in motion, no matter where your data resides.
From real-time fraud detection, financial services, and multi-player games, to online social networking, Confluent lets you focus on deriving business value from your data rather than worrying about the underlying mechanics of how data is streamed, integrated, stored, and connected at scale.
Data collection is only one piece of the puzzle. Today’s enterprise businesses simply cannot wait for data to be processed in batch form. Instead, everything from fraud detection and stock market platforms, to ride share apps and e-commerce websites rely on real-time event streams.
Paired with streaming data, applications evolve to not only integrate data, but process, filter, analyze, and react to event as they happen in real-time. This opens a new plethora of use cases such as real-time fraud detection, Netflix recommendations, or a seamless shopping experience across multiple devices that updates as you shop.
In short, any industry that deals with large volumes of real-time data can benefit from continuous, real-time event stream processing platforms.
Stream processing systems like Apache Kafka and Confluent bring real-time data and analytics to life. While there are use cases for event streaming in every industry, this ability to integrate, analyze, troubleshoot, and/or predict data in real-time, at massive scale, opens up new use cases. Not only can organizations use past data or batch data in storage, but gain valuable insights on data in motion.
Typical uses cases include:
As long as there is any type of data to be processed, stored, or analyzed, a Confluent can help leverage your data for any use case, on any scale.
Confluent is the only complete data streaming platform that works with 100+ data sources for real-time data streaming and analytics. Deploy on your own infrastructure, multi-cloud, or serverless in minutes with platinum support.