Why Event-Driven Architecture Is a Key Component of Data Science
Data science is a field that relies on scientific methods, processes, and computer algorithms to find patterns in structured and unstructured data sets in order to extract actionable insights. Data science is used in a wide variety of fields, and any modern enterprise needs to rely on it due to the rapid growth of big data. The term “big data” refers to massive volumes of data that can’t be processed by legacy systems or traditional methods. Data science can be broken into five basic stages:
Capture: Data acquisition and entry.
Maintain: Data warehousing and cleansing.
Process: Data mining and classification.
Analyze: Data regression, qualitative analysis, and predictive analysis.
Communicate: Data reporting and visualization.
Enterprises across all industries need professionals who can manage massive amounts of data. They also need technology capable of capturing and analyzing data in real-time. This is where event driven architecture (EDA) comes in.
What is EDA?
Put simply, EDA is a modern software design pattern that’s able to detect events and act on them in practically real-time. This approach replaces the traditional request/response architecture pattern. With request/response, one computer would send a request (such as access to data) to another computer system on the network, and it would have to wait for the response (access granted or denied) before it could move on to another task. This may have been serviceable back when organizations dealt with smaller data sets, but now such wait times are unacceptable.
Today, organizations deal with constant event streams. An event is basically any important business moment, such as a financial transaction, a click on a website, or a change to a shopping cart. EDA is the only way to react to complex events quickly enough to get customers through them at a satisfactory pace. With this technology, a computer system can handle multiple simple events at the same time and move on to other tasks without having to wait for a response.
How does it work?
EDA is frequently used in microservice architecture. Essentially, microservices are applications that are built around loosely coupled services. This means that each service is able to work independently, but they also work together in the app. This is made possible thanks to sharing API architecture. The purpose of decoupling is to ensure that the actions of one microservice aren’t able to slow down the entire application.
Whether it’s a simple or complex event, everything starts with an event producer. This could be your company’s retail website, your mobile app, or even your in-store point of sale system. When a user places a new order, submits a question to customer support, or purchases an item in person, an event is produced. This event is then sent to the event router, which collects and filters events, so each event type can continue down the line to the right consumer. The event consumer might be the customer themselves if they’re placing a return, or it could be your inventory management system that needs to update the stock. Your financial systems are also event consumers whenever they have to make updates based on sales and returns.
Of course, events aren’t just created by customers. An event can also be triggered due to unusual activity on your network. A security alert about a hacking attempt, for example, is also an event. Even something as simple as a user login will trigger an event. Events can also be triggered by IoT devices, such as sensors on your manufacturing floor. In this case, an event notification could alert your staff to possible maintenance issues, such as if the sensor detected unusual temperatures. Ultimately, an event driven framework is meant to make your enterprise more agile and scalable so you’ll be able to adapt to future needs, and it’s the only reliable way to process big data.