If you’re studying for a tech degree, it can feel like there are an infinite number of programs and platforms to educate yourself on. Being able to identify which technologies will be most relevant to your future career and getting a head start on learning to use them is a great way to be proactive about your career as a student. Whether you’re focusing on information technology, data science, or another related field, you’re likely to need to become familiar with Apache Kafka. Given that many businesses are moving toward event-driven platforms like Apache, rather than traditional databases, it’s likely to become even more widely adopted in the future. Read on to learn more about what Apache Kafka is and what applications it may have within your chosen field.

What is Apache Kafka?

If you’re new to data science or studying tech, you may be wondering, what is Kafka? Apache Kafka is an open-source distributed publish-subscribe messaging platform. Developed by the Apache Software Foundation, the platform is written in Scala and Java. It was built for the purpose of handling data-streaming in real time for distribution, pipelining, and replay of data feeds in an efficient and scalable way.

Logs are typically used when storing data categorized by events, and Apache Kafka is a system that allows you to manage those logs. Kafka organizes these logs into topics, which is an ordered collection of events that are stored by being written to a disk and replicated. This ensures that a hardware failure will not erase all of your data. Topics can store data for just a short period of time or indefinitely, and their size can be either small or as large as needed. Kafka is what’s known as a broker-based solution, as it records and holds data streams within a cluster of servers.

How is Apache Kafka used?

There are a number of fields that make use of Kafka’s capabilities. While it started out primarily being used for messaging, it has evolved into a fully equipped event streaming platform. Its primary use cases still revolve around creating reliable data exchange, streaming for data processing, being able to partition a messaging workload, and providing native support for message and data relay. Analyzing the data streams from your topics in real time is more efficient for businesses than running a batch process overnight.

It’s important to understand what “events” are in the context of your business, as the term can refer to a variety of actions or occurrences that need to be logged and analyzed. Some examples of events include a user updating their information or billing address or an in-home thermometer alerting the user that the temperature is getting dangerously cool. Kafka encourages you to think about business in terms of events, whereas a database would have you think in terms of individual things (like the user, the thermometer itself, etc.) instead.

While databases used to be the standard for handling data in business, many companies are moving towards event-driven logging. Platforms like Apache Kafka enable you to organize logs of your data into topics, which can be analyzed in real time. Being able to use Kafka is necessary for anyone who wants to work in data science or technology. It’s a worthwhile pursuit to educate yourself in Apache Kafka and some of the other platforms you’re likely to need in your career while you’re still in school. Not only will you be better prepared to set out on your own business ventures, but it can also give you a leg up in difficult classes and help you bolster your résumé when applying for competitive internships and other work opportunities.

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