Apache Kafka is great for capturing real-time data in motion. Hazelcast lets you build applications that act on Kafka data immediately, instead of just moving it into a database.
You chose Kafka to handle your real-time data. You clearly see value in recently created data. What if you used Kafka to automate, streamline, and enhance your business with real-time capabilities?
For example, with Hazelcast you can:
Grow revenue by acting on fleeting opportunities that you would otherwise miss
Mitigate risk by responding to time-critical threats and challenges
Boost return-on-investment of your data-driven strategies
Reinforce Kafka as your foundational technology of the future
This is why Kafka is better with Hazelcast, the real-time stream processing platform.
Why Hazelcast
Why choose Hazelcast to get your Kafka investment to the next level? Why is Hazelcast a better choice than other stream processing engines? There are 3 strong technology advantages that make Hazelcast the ideal choice to boost Kafka:
Its simplified, integrated architecture. Hazelcast combines key components in a Kafka ecosystem into a single architecture. This means your developers spend more time on business logic and business value, not on infrastructural maintenance. Instead of trying to keep disparate parts working together, they focus on building apps that help you grow revenue.
Superior speed, scale, and efficiency. Hazelcast performance advantages mean you get more output from your hardware, making it a more cost-effective choice than other technologies. Publicly documented benchmarks (like this one from the Hasso Plattner Institute) demonstrate the Hazelcast advantage over other popular technologies.
Broader functionality. Hazelcast supports more capabilities and more types of use cases than other single-purpose technologies. This lets you immediately grow into new use cases that drive business value without having to reevaluate your stack.
Everyone Likes Real-Time Value
“I don’t have real-time requirements.” This is an all-too-common response from otherwise data-savvy professionals. Below are the comments we get:
“Real-time capabilities are hard and expensive to add, and I’m not prepared for that.”
Real-time capabilities are only hard and expensive if you use batch-oriented technologies. If you use the right technologies. i.e., a real-time stream processing platform, you get the value you seek from real-time data.
“The benefits of real-time capabilities to my business are not clear.”
Start with the business outcomes you want. Will your business gain if you can respond to time-sensitive opportunities hidden in data? Can you grow revenue and cut costs if you respond to customers while they are interacting with you? If so, it’s worth exploring more about how to respond more quickly to your Kafka data.
“I don’t need to respond to customers in milliseconds.”
Real-time responsiveness is not about ultra-low latency. It’s about automatically responding to an opportunity when it matters most, whether that’s milliseconds or even minutes. It’s about:
A popular architectural use case for Apache Kafka is real-time analytics. The process flow entails moving your real-time data into Kafka and then into a database. From there, human analysts query for actionable insights.
This is a perfectly useful, but it’s only part of the Kafka story.
Consider the advantage you’d get if you created more immediacy around your real-time data and did not have to wait for data to be stored in a database, or wait for data to be analyzed by humans? There are plenty of opportunities, and here are just a few examples:
Real-time offers or recommendations while customers are interacting with your website
Fraud detection that leverages machine learning algorithms to catch fraud while the transaction is being processed
Predictive maintenance to identify potential equipment failures before they occur
Customer 360 views that are kept up-to-date continuously, not just per hour
Connected vehicle analysis that enables real-time rerouting based on traffic jams, or alerting due to distracted driver signals
Internet of things analytics that enable adjustments to operations without human intervention
Logistics tracking to provide immediate status alerts to allow time to adjust for alternative plans
These use cases are possible because you already have real-time data stored in your Kafka deployments. Just add Hazelcast to immediately identify and respond to those actionable insights. Check out this blog, Going Beyond Real-Time Analytics, to see how you can get more value from your data in motion.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.
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