World-wide real-time data grows exponentially every minute as more people use more devices, websites, IoT sensors, and other ways to stay connected. Today, the ability to instantly analyze and capitalize on streaming data, precisely when it is generated, is key to the success or failure of an organization. While the customer is still king, real-time data processing supplies the king with the most relevant information possible, delivered in microseconds. Real-time data processing is no longer the future – it is the present.

Real-time data streaming is the continuous, neverending flow of data that supplies a constant feed of information that can be analyzed and acted upon without being downloaded. Examples include e-commerce websites, which capture ongoing information about users and their actions; streaming entertainment and sports; ride-share applications; banking transactions; social networks; stock trading; and online gaming where players connect around the globe. Online gaming companies use streaming data to evaluate player interaction and offer incentives or other dynamic experiences. In live sports, analytics capture data about player actions, predict scores, and supply updates in real time. Banks use real-time data processing to ensure secure and accurate transactions 24/7/365. Real estate sites track consumer search behavior and make real-time property recommendations that meet their criteria.

Three years ago, Americans were using 4,416,720 GB of internet data daily, including 188 million emails, more than 18 million text messages, and nearly 4.5 million Google searches every single minute. Skype users made 231,840 calls and Twitter users posted 511,200 tweets a minute. According to Grandview Research, the video streaming market size will grow at an estimated compound annual growth rate of 21% to reach an estimated $223.98 billion in value by 2028. 

For today’s businesses, continual analysis of data streaming is critical, as it can provide new and disruptive competitive advantages, facilitate the ability to react in real time, enable automated decisions and, most importantly, help organizations move and stay ahead of the competition. 

Batch Processing vs. Event Stream Processing

Traditional data batch processing taps into stored data, completes the analysis, and provides results that can initiate action. The creation of a monthly bank statement is an example of batch processing. It provides only a “snapshot” of what is in the data at a specific time.  Continuing the banking example, real-time data processing occurs when an electronic payment is made, and the customer can see the payment deducted from their bank account balance immediately. Real-time data processing provides instantaneous, precise results.

According to Science Direct, event stream processing is a set of methods that enables an alternative to series of queries against static data sets. An event stream processing framework can be configured to monitor high volumes of data flowing through multiple continuously flowing input data sources with very low latencies for event processing. An event stream processing engine can evaluate both transient events as well as events that are retained within a virtual cache. Input streams are continuously pushed to a query processor that essentially “reacts” to arriving data. Multiple patterns can be scanned simultaneously, because as new events arrive, they are routed through a set of continuous queries.  

Event stream processing enables many different applications, such as algorithmic trading in financial services, radio-frequency identification (RFID) event processing applications, fraud detection, process monitoring, and location-based services in telecommunications.

Advantages of Real-Time Event Stream Processing

Real-time event stream processing delivers precisely targeted information that enhances the customer experience and customer service, accelerates innovation, increases sales, and boosts the survival rates of organizations that use it. Several industry leaders have used event stream processing to excel and take over markets. Google generates most of its revenue from ads that appear in search results. It uses this methodology to deliver relevant ads in real time to users based on search history, prior purchases, product availability, and even geo-specific weather data. 

A few examples of event stream processing success stories include:

• Amazon, the leader of event stream processing, delivers multiple choices and recommendations in real time to shoppers based upon prior purchases, searches, and other factors. 
• Netflix crushed the video rental industry and used event stream processing to revolutionize home entertainment industry with on-demand video streaming customized to individual viewers. 
• Uber and Lyft transformed urban transportation and use it to track driver and passenger locations as well as process payments.
• Tesla uses it to enable self-driving mode, tracking vehicle location and movements, and make corrections when necessary.
• Online gaming and live sports are driven by stats generated by event stream processing.
• Commercial real estate buildings use it and IoT sensors to predict and address maintenance/building comfort issues before they impact residents.
• Stock market analysts rely upon it to identify opportunities and threats in the market.
• Credit card companies and banks use it to stop fraud.

Event stream processing also enables companies to future-proof their operations and fend off new competitors who may enter the market. The ability to mine stockpiles of legacy data, while at the same time using the technology to identify futuristic consumer trends, provides leading tech organizations an edge in the marketplace and makes it extremely difficult for newcomers to compete. Not only do companies have immediate access to historical data patterns, they can also use those old patterns to compare to the event stream processing data points and identify new trends or new products before a competitor can launch a serious threat. 

Some well-known brands did not adopt event stream processing, which led to their ultimate demise: Pets.com, Blockbuster, newspaper and yellow pages advertising, and thousands of others who fail every day because someone else used event stream processing to better serve their marketplace.

Tools and Challenges for Real-Time Event Stream Processing

Event stream processing tools are software and systems that support real-time data stream processing. They select and store events and analyze those events in real time to identify critical factors such as internet searches, purchasing patterns, fraud, weather changes, etc. The technology makes it easier for developers to write applications that can act upon the events identified by ESP. Software applications such as Apache Kafka, Amazon Kinesis, Apache Storm, Azure Stream Analytics, and others are currently mainstays in this arena. 

The number-one challenge event stream processing faces is the overwhelming amount of data generated and the exponential growth of streaming data. Massive amounts of data must be processed in real time to identify rapid consumer spending shifts, capitalize on those trends, or identify the ideal consumer target for a new product or service. The software must be scalable, deliver consistently reliable results, and avoid faults. Also, many of these systems are asked to tap into existing legacy data to leverage historical information that may help define current data streams.

The amount of data generated will continue to increase every minute of every day. Today’s customers make rapid-fire decisions based upon highly customized information tailored to their exact desires, needs, and behaviors. Purchasing activity evolves based upon time of day, holidays, special events, changing personal preferences, stage of life, and other factors. Real-time data represents a gold mine to those willing and able to find the treasure. 

Real-time data stream processing is no longer an option for businesses that want to survive and excel. Successful organizations depend upon and leverage real-time data to instantly adapt to customer needs and desires, innovate based upon changing demands or interests, and survive against ever-evolving competition. Data is powerful and instant information reigns supreme. It drives microsecond customer purchasing decisions, identifies rapid changes and new opportunities in the marketplace, helps avoid fraudulent transactions, and spotlights the next hot stock before it surges. Real-time data processing is now the key to innovation, growth, and survival.