Do I Have Time to Learn Event Streaming?

Author: Pulsar Neighborhood
Published: May 13, 2022

A few years back, data used to be collected, stored, and later on analyzed to get meaningful insights. In fact, businesses that did this were seen as data-guided. But today, data collection, analysis, and storage all run concurrently. Static applications that require regular refreshing to understand the current state of events are becoming a thing of the past. We want the latest observed data to be reflected in the insights and messages we’re receiving.

Event streaming is a way for us to receive insights based on the most up-to-date data. With event streaming, we can see and use data points that emerge from a system that continuously creates data in near real-time to identify and establish patterns within those streams — such as the relationships or hierarchies between events.

In this article, we’ll explore what event streaming is, how it works, and discuss the growing importance of event streaming. Then, we’ll highlight how we can simplify the task of learning event streaming with the help of Apache Pulsar.

What Is Event Streaming?

Before we dive into what event streaming is and how it works, let’s look at what the event in event streaming is.

An event is a data point that’s repeatedly generated by a system after a specific occurrence. For instance, when we read a new email, we trigger an event that changes its state from “unread” to “read.” Similarly, when we like a tweet, we trigger an event that marks it as “liked” on our side, and another event that increases the number of “likes” as seen by everyone.

Event streaming describes the continuous streaming of those data points. In the world of application development, it’s called a data stream. The data in these streams has temporal durability that ensures that the system isn’t removing it as soon as it’s processed.

Traditionally, systems only shared data on demand. This meant that services only communicated with each other when they needed something from each other. They operated on an “ask, don’t tell” policy.

In contrast, an event-driven architecture follows a “tell, don’t ask” policy. This means that instead of one service querying another every time it needs to read something, it can just subscribe. Subscribing gives it continuous access to capture what it needs from other services’ data and collate it if necessary.

The popularity of event streaming has also been supplemented by the need for solutions that can unleash the full potential of modern application architectures, such as Kubernetes. Without a doubt, event streaming represents the current and future approach to data handling.

Event Streaming Use Cases

To help illustrate event streaming in action, let’s look at an example where an online purchase is processed. In the front end, only one action takes place: the customer clicks “buy.” On the back end, the system does several things:

  • It marks the item as “sold.”
  • It marks the order as “to be shipped.”
  • It reduces the stock available by the quantity that was purchased.
  • It increases the revenue amount.
  • It updates the customer’s purchase records.
  • It sends the customer an email confirming the purchase.

In this example, a single purchase triggers multiple critical events that are necessary to efficiently run an online business.

Event streaming is also used by ride-hailing apps like Uber and Lyft, which match customers to drivers based on real-time streamed GPS coordinates.

It’s not just e-commerce and the logistics/transportation industries that use event streaming. We can see event streaming playing a significant role in the financial sector (processing transactions in real time), manufacturing (capturing and analyzing data from sensors, for example, on equipment), and healthcare (monitoring patients and updating patient charts to include predictions for future care needs).

The popularity of interactive applications that rely on event streaming has increased tremendously across different industries, resulting in increased demand for people skilled in implementing it. So, why should you learn event streaming?

Why You Should Learn Event Streaming

The world of software development has evolved drastically in the last decade. Some of the major changes include:

  • The rise of open source software.
  • The replacement of waterfall methodology with agile development.
  • The move from on-premises to cloud computing.
  • The introduction of real-time data streaming and processing.

As developers, we want to possess skills that are marketable today and have long-standing relevance. Currently, event streaming is one of the most marketable skills because it’s widely used in modern application development. Being familiar with how event streaming works and how to implement it efficiently enables you to add business value to your organization, while also providing you with a skillset that you can carry into future opportunities.

However, learning event streaming isn’t easy as it has several complex components. This is largely due to the complexities that can accompany event-driven architectures — even more so if you’re working with a multi-cloud environment or with microservices. Additionally, because event streaming requires a completely different approach from traditional, monolithic architectures, your knowledge of traditional systems doesn’t give you much of an advantage.

Several routes are available to developers who want to learn how to implement event streaming. It’s important to select an event streaming solution as well as educational tools that ease the learning curve and offer longevity. When choosing the event streaming solution to learn, you want to ensure that it won’t soon be outdated from a lack of compatibility with new technologies and that you’ll stay up-to-date on the technical know-how as new technologies are introduced.

Apache Pulsar is an open source, cloud-native event streaming solution that works seamlessly with modern application architecture. It transformed event streaming by layering data processing and data storage. This resulted in a system with low latency, high throughput, easy scalability, and high availability. All these qualities, alongside its simplified learning curve, make it very popular for event streaming.

Learning Apache Pulsar

If you’re just getting started with event streaming, you’ll find that learning about it is easier with Apache Pulsar. Let’s explore some of Apache Pulsar’s most beginner-friendly event streaming features.

To begin, Apache Pulsar requires only a few lines of code to build a microservice. To do this, we use lightweight compute processes called Pulsar Functions. These are Lambda-style functions created specifically to work as a message bus and can be deployed in Kubernetes pods, processes, or threads.

Pulsar Functions can be written in Python, Java, or Golang. These are all extremely popular languages that are well-documented and straightforward to learn and use. It’s important to note that Pulsar functions aren’t tied to each other, meaning they can be scaled independently depending on the workload they are expected to handle. We can also easily test our Pulsar Functions, which makes it easier to understand the impact of different configurations.

Additionally, Apache Pulsar’s cloud-native architecture separates storage and computing into two different layers. This makes it easy to scale and integrate with external libraries, frameworks, and libraries. Moreover, we can also connect Pulsar microservices to MongoDB, InfluxDB, Aerospike, Redis, ElasticSearch, and protocol handlers like AMQP, JMS, and MQTT.

How Apache Pulsar Handles Event Streaming

So how does Apache Pulsar handle event streaming?

Event-driven microservices, like those built with Apache Pulsar, use a message bus to relay messages between the loosely coupled collaborating services. As each service performs a task that might be relevant to other services, Apache Pulsar produces an event. The consumer services then use the information from that event to accomplish their own tasks.

This process makes the message bus the only path through which different services can communicate. It receives the events triggered by producers, filters them, and then forwards them to consuming services. It’s important to note that the producer isn’t tied to the events that are passed in the message bus. For it to be effective, it has to support different consumption patterns, messaging paradigms, and subscription types.

Apache Pulsar is best suited for event-driven microservices because it supports different message patterns, thanks to its diverse nodes and subscription types.

Additionally, Apache Pulsar handles event streaming within one system. This gives it an edge in cost savings over traditional systems that require the deployment and management of multiple systems to do similar functions.

Conclusion

An event stream is formed by a series of frequent individual events — data points generated by a system after a specific occurrence — at a known point in time. Event streaming, then, allows for the development of interactive applications reflecting the current state of the underlying data.

The continued growth in demand for modern, real-time applications has made event streaming skills very valuable and marketable across numerous industries. But to make the most of your work with event streaming, you need an approachable, forward-looking educational guide and streaming solution.

As you’ve seen in this article, learning event streaming doesn’t have to be a strenuous or time-consuming exercise when you’re using Apache Pulsar. Learn more about how Apache Pulsar enables you to implement event streaming efficiently.

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