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Streaming data is everywhere, and today’s developer needs to learn how to build systems and applications that can ingest, process and act on data continuously generated in real-time. Developers are tapping into endless data streams to solve operational challenges, build delightful customer experiences and build new products, which means the learning opportunities are endless too.
One of the most widely adopted data streaming technologies is Apache Kafka. In the 12 years since this event streaming platform was open-sourced, developers have used Kafka to build applications that transformed their categories.
Think Uber, Netflix, or Meesho. Developers working with data streaming in these kinds of organisations and the open-source community have created applications that track locations instantly, deliver personalised content and process payments in real time.
These real-time capabilities are so embedded into our daily lives that we take them for granted. Before bringing those capabilities to life, developers first had to understand this platform and how to best take advantage of it.
A deeper understanding of how Kafka works
Apache Kafka can record, store, share and transform continuous streams of data in real-time. Each time data is generated and sent to Kafka, this “event” or “message” is recorded in a sequential log through publish-subscribe messaging.
While that’s true of many traditional messaging brokers, Kafka is designed to deliver messages at network-limited throughput, scale to trillions of messages per day, store those data streams and provide the storage and compute elasticity required of today’s internet-scale applications.
When client applications generate and publish messages to Kafka, they’re called producers. It’s automatically organised into defined “topics” and partitioned across multiple nodes or brokers when stored. Client applications acting as “consumers” can then subscribe to data streams from specific topics.
These functionalities make Kafka ideal for real-time ingestion and processing of large volumes of data such as logistics, retail inventory management, threat detection and better customer experiences. The architecture enables organisations to democratise their data, giving developers and other data-streaming practitioners access to shareable streams of data from across their organisations.
Skills a developer needs to effectively use Data Streaming
Kafka provides client libraries that simplify the reading, and writing of streams of data in a variety of programming languages. Kafka streams enable developers to easily process these data streams in real time. Kafka Connect enables developers to easily bring in data from their data sources and turn data-at-rest into data-in-motion.
Confluent completes Kafka. ksqlDB further simplifies stream processing allowing developers to use SQL to process the data streams. With Schema Registry, Stream Catalog and Stream Governance, developers can confidently democratise data within their organisations.
Once developers have mastered creating data pipelines with Kafka, they’ll be ready to explore streaming processing, which unlocks a host of operational and analytics use cases while creating reusable data products.
Learn to problem-solve with a data-streaming mindset
Instead of thinking of data as finite “sets of sets,” as they’re stored in relational databases, you’ll have to learn how to apply data stored as immutable, appending logs.
Kafka is one of the most active open-source projects, and businesses across sectors are doubling down on their investment in data streaming. With so many companies and industries standardising Kafka as the de facto solution for data streaming, there’s a robust community for newcomers to join and learn alongside.
Developers who invest time in learning how to solve these kinds of impactful use cases will have a wealth of job opportunities, which means more interesting problems to solve and space to grow their skills and careers.
Learn on the ground and in real-time
We want to show you how Kafka can work for you to help your business work in real-time. That’s why we are bringing the Data in Motion (DIM) Tour to Bengaluru and Mumbai in June. This tour is for data-driven startups and enterprises that want to learn how data streaming can enable real-time analytics and decision-making with the power and flexibility of the cloud.
Whether you are new to Kafka or a seasoned pro, if you are ready to take your Kafka skills to the next level and learn how to leverage data streaming in the cloud, don’t miss the Data in Motion Tour. You will find something valuable and inspiring there.
