Event streaming: Tips for building event-driven apps

Event streaming sets the foundation for real-time data processing. This streamlines the process of integrating and interpreting crucial information to keep applications in prime condition. 

With the digital landscape constantly evolving, event-driven architecture continues to grow in popularity, helping organizations keep up with changes and make real-time, data-driven decisions that benefit their business. This article covers event streaming, tips for designing an event streaming architecture and how to use it to your advantage when building event-driven apps.

Event stream processing is a technology that handles and interprets a continuous flow of real-time data as an event or change occurs. By processing singular data points rather than large batches, event streaming apps enable software to perceive, respond to and manage events as they happen. This process is useful for systems requiring immediate responses to data changes, including e-commerce, real-time analytics or IoT. Essentially, it helps businesses conduct faster analyses and make informed decisions when a prompt response is needed for large volumes of data.

Before incorporating event streaming architecture into your own applications, it's important to understand key concepts to make the process easier.

In event streaming, real-time data from multiple sources is processed simultaneously, offering immediate insights and facilitating data-driven decision-making. This process helps businesses instantly respond to data flowing into their organizations. This, in turn, helps companies build successful, reliable applications, which is a necessity in the digital era.

Event-driven architecture (EDA) is an architectural pattern that detects and reacts to events to determine a program's flow. Certain events, such as user actions, sensor outputs or messages from other programs, play a key role in helping this process function. The architecture is supported by asynchronicity and fosters flexible, decoupled systems that flow and scale seamlessly in conjunction with business activities.

A message broker is an architectural pattern enabling systems and applications to communicate and exchange information with each other. Systems, such as Apache Kafka, translate messages from the sender's protocol to the receiver's protocol, ensuring a reliable and balanced data transfer. This separation between data processors and data producers helps businesses create flexible, scalable applications that work more efficiently.

This is a cloud computing model that eliminates the need for developers to manage infrastructure when building applications personally. Instead, the cloud service provider automatically provisions and scales the infrastructure, allowing developers to focus on other tasks, such as writing and deploying code.

CDC is a software design pattern set that monitors and tracks data changes. Those changes are then delivered to a downstream process or system in real-time. These procedures can be replicated, making this a highly efficient way of moving data across a vast network to assist with data analytics and decision-making.

Apache Kafka is an open-source event streaming platform developed by the Apache Software Foundation and written in Java and Scala. This system is used  by thousands of companies worldwide. It provides multiple services, such as high-performance data pipelines and streaming analytics, making it a beneficial tool for building event-driven applications.

If your company is struggling to keep up with information flow and needs more tools to make decisions, event streaming may help to streamline processes due to its use of cloud-based streaming. For reference, cloud streaming means storing and transferring data from one source to another. Take a look at the benefits of using event streaming architecture and what situations it can help with.

Since event stream processing happens in real-time, it provides a range of benefits for businesses regarding handling data. These benefits include:

• Handling large amounts of data: Data streaming architecture processes data in real-time to provide the most up-to-date information available on the application. This ensures that businesses are aware of crucial events or changes as they happen.

• Enabling timely business decisions: Because event streaming makes relevant information available as it happens, businesses can make data-driven decisions without delays. This prevents disruptions and helps companies continuously evolve and act while keeping in step with their data.

• Supporting decoupled, scalable architecture: When using data streaming applications, you won't have to worry about how quickly business demand increases. Event streaming is designed to support decoupled, scalable architecture to help each independent process run smoothly and efficiently, no matter what events occur.

• Enhancing reliability: Event streaming can enhance a system's reliability and resiliency by continuously monitoring data and interactions to pinpoint and eliminate issues as they arise. This keeps systems running at optimal levels without needing a developer to manually track them.

• Balancing more work without compromising performance: Juggling multiple tasks simultaneously increases the risk of making mistakes or compromising work performance. Event stream processing has the ability to handle an increased workload without human error.

Event streaming can help solve business issues across numerous industries, from e-commerce to financial services. Event streaming examples might include:

• Tracking pricing and analytics 

• Overseeing fraud detection and risk management

• Monitoring live dashboards for instant business insights

• Detecting system anomalies to maintain security 

• Conducting event sourcing to restore systems to original conditions

• Overseeing real-time system functions to prevent issues or disruptions

From balancing large amounts of data to automatically updating systems, event stream data models and architecture can improve your applications’ functionality, reliability and resiliency. Here are several tips on using event-driven applications and how they can benefit your business.

Message brokers serve as vital links in event streaming architecture because of their high throughput and ability to handle large volumes of data. 

Unlike traditional message queues, message brokers can store past events, allowing users to reference historical data when making decisions about current situations. This ensures that messages won't be lost if a disruption or failure occurs. Additionally, it provides businesses with a blueprint of how to restore or reconfigure the system in these scenarios.

For instance, you might decide to use Kafka to reconstruct an application's condition after a system failure, returning the application to its original functionality. Kafka’s message broker feature enables businesses to resolve issues and resume normal operations as soon as possible, preventing major setbacks and mishaps.

Since applications can undergo many changes, change data capture (CDC) will help you track these changes in a data source so they can be captured in a system. A CDC system identifies and captures data updates in real-time, providing businesses with fresh insights as these changes happen. This gives businesses the most up-to-date and accurate information available to them, which is beneficial in situations when prompt decision-making is required.

Understanding and utilizing CDC is an effective way to ensure data consistency across multiple systems in real-time, enhancing the functionality of event-driven applications. CDC is a vital tool in synchronizing changes across systems and maintaining audit trails for regulatory compliance. It can also identify and track new developments or discrepancies.

Implementing CDC into existing applications provides additional benefits, such as:

• Minimizes disruptions to production workloads for more efficient processes

• Accelerates business intelligence and reporting capabilities for faster data collection

• Helps integrate software tools with otherwise incompatible databases, providing more flexibility when choosing business applications

• Improves an organization's master data management system by constantly refreshing and updating critical data

Serverless architectures, such as AWS Lambda, are essential components of cloud-based, data-driven applications due to their ability to run applications and services while eliminating the need for developers to perform these tasks manually. AWS Lambda can process event streams, scales with demand and can be configured to respond automatically to changes.

Serverless architectures are equipped with multiple features for simplified operations. For starters, serverless apps provide automated solutions for building and running applications, eliminating the need for constant server oversight and management. This enables developers to spend more time and attention on delivering value instead of performing maintenance. Using serverless architectures can also reduce operational costs, since AWS Lambda only charges for computing time used.

Additionally, serverless models adapt exceptionally well to event-driven architectures. When AWS Lambda is used with other services, such as Amazon Kinesis, AWS Data Migration Service or DynamoDB Streams, data records can be processed in real-time upon arrival. This feature opens up new capabilities, including monitoring real-time analytics, making timely data-driven decisions and ensuring responsive application behaviors. A key strength of event-driven architectures is their ability to promptly and reliably handle large volumes of events. Serverless technologies play an essential role in making this possible.

Event streaming has made it possible to develop responsive, resilient and scalable applications that transform the digital landscape for cloud-based technologies and change how organizations conduct business.

Learn more about the ways Capital One uses cutting-edge technologies to build apps in the cloud on our blog.