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Spring Cloud Stream for Real-Time Event-Driven Systems

Mon, 21 Jul 2025 13:33:34 +0000

Envision operating a successful e-commerce platform where every moment is crucial. Customers make purchases, adjust inventory levels, process payments, and dispatch shipping alerts—all in real time. In the background, microservices diligently function to guarantee seamless operations. However, how do these services interact effectively, particularly during peak traffic occurrences such as flash sales or holiday shopping events?

An efficient method to execute this use case involves utilizing Spring Cloud Stream (SCS) as the messaging infrastructure. SCS facilitates seamless interaction among microservices, enhancing the system’s responsiveness and scalability. A significant benefit of SCS is that it provides an abstraction layer, allowing developers to transition between messaging technologies such as Kafka or RabbitMQ or Pulsar or Amazon Kinesis without altering their fundamental business logic.

Spring Cloud Stream: Demystifying Event-Driven Architecture

Fri, 28 Feb 2025 08:34:32 +0000

Consider a bustling restaurant environment. The kitchen receives a flood of orders, waitstaff dart between tables, and patrons anxiously glance at their watches. Each time a waiter takes an order, he or she must verify it with the kitchen and wait for the kitchen to prepare the meal before attending to another table. This cumbersome method hampers efficiency, leaving the restaurant unable to meet customer demand.

Now envision an alternative situation. Instead of enduring delays, waitstaff enter orders into a centralized system, prompting the kitchen to begin meal preparation immediately upon receipt. As soon as the kitchen prepares the food, waiters receive instant notifications, enabling them to serve customers promptly without any delays. This exemplifies event-driven architecture: a fluid, real-time system where they exchange information asynchronously, promoting scalability and quick responsiveness.

Real-time Stream Processing with Hazelcast and StreamNative

Wed, 01 Feb 2023 09:09:04 +0000

Introduction

One of the most useful features of real-time stream processing is to combine the strengths and advantages of various technologies to provide a unique developer experience and an efficient way of processing data in real time at scale.

Hazelcast is a real-time distributed computation and storage platform for consistently low latency queries, aggregation and stateful computation against real-time event streams and traditional data sources.
Apache Pulsar is a real-time multitenant geo-replicated distributed pub-sub messaging and streaming platform for real-time workloads handling millions of events per hour.

However, real-time stream processing is not an easy task, especially when combining multiple live streams with large volumes of data stored in external data storages to provide context and instant results.

Unified Event-Driven Architecture for the Cloud Native Enterprise

Thu, 06 Oct 2022 19:58:06 +0000

CTOs and enterprise architects have long recognized the importance of event-driven architectures (EDA). While once considered purely a technology concern, the foresight of organizations that have invested in EDA has become readily apparent as the world has shifted around us. In the past decade, we’ve witnessed changes in nearly every aspect of our technological worlds, and the vast majority of those have been affected in some way by a move toward event-driven, real-time processing.

Why Developers Should Use Apache Pulsar

Thu, 19 May 2022 15:10:49 +0000

Apache Pulsar is an open source streaming platform that addresses some important limitations in Kafka, particularly for cloud-native applications.
SPainter VFX / Getty Images

If you are building applications today, you are probably familiar with the microservices model: Rather than building big monolithic applications, we break services down into isolated components that we can independently update or change over time.

Microservices deployments then can use a message bus to decouple and manage the communication between services, which makes it easier to replay requests, handle errors, and deal with load spikes and rapid increases in requests while maintaining the serialized order.

5 More Reasons to Choose Apache Pulsar Over Apache Kafka

Thu, 05 May 2022 18:11:20 +0000

Author’s note: I originally published this blog post in 2019, while I was CEO of Kesque, a real-time messaging service built on Apache Pulsar , the cloud-native distributed messaging and streaming platform. It’s a follow-up to an earlier post, “ 7 Reasons to Choose Apache Pulsar over Apache Kafka .” A lot of big changes have happened since these two posts went live, including Kesque’s acquisition , in January 2021, by DataStax. The reasons to choose Pulsar, however, haven’t changed.

Bring Streaming to Apache Cassandra with Apache Pulsar

Thu, 05 May 2022 18:10:42 +0000

Twitch, YouTube, Instagram, Facebook — virtually every major brand nowadays uses live streaming to connect and engage their audience.{#2544}

For enterprises and developers building cloud-native applications , this growing trend creates a need for streaming technologies that can reliably handle the rush of massive amounts of data, while also being flexible and easy to manage for developers.{#2544}

One such technology is Apache Pulsar ® — an open-source, distributed messaging and streaming platform that’s easy to deploy, simple to scale, and packed with developer-friendly APIs.{#8b5a}

Full Stream Ahead: Astra Streaming, Powered by Apache Pulsar

Thu, 24 Mar 2022 09:22:15 +0000

With a serverless architecture, multi-region geo replication, and Apache Cassandra’s legendary performance, DataStax Astra DB makes it easy for developers and enterprises to start small with their applications and grow them to infinite scale without constant performance tuning and optimization exercises.

However, cloud app developers also face significant challenge`s beyond data at rest. Modern data apps require high-scale streaming technologies that can deliver the reactive engagement at the point of interaction that end users have come to expect. That’s why today, we’re excited to announce that these capabilities have been added to DataStax Astra.

Why Pulsar Beats Kafka for a Scalable, Distributed Data Architecture

Thu, 03 Mar 2022 14:10:45 +0000

The leading open source event streaming platforms are Apache Kafka and Apache Pulsar. For enterprise architects and application developers, choosing the right event streaming approach is critical, as these technologies will help their apps scale up around data to support operations in production.

Everyone wants results faster. We want applications that know what we want, even before we know ourselves. We want systems that constantly check for fraud or security issues to protect our data. We want applications that are smart enough to react and change plans when faced with the unexpected. And we want those services to be continuously available.

The Critical Role Streaming Plays in a Data Stack

Fri, 18 Feb 2022 17:20:13 +0000

Why Apache Pulsar is the right choice for multi-datacenter, geo-distributed deployments.

Building applications today requires companies and teams to be familiar with data flows (including streaming data flows) and how to transform them into compelling user experiences. Data fuels companies’ ability to understand their customers, move faster than competitors, and provide compelling products that the market wants. According to research by DataStax and Clearpath Strategies, almost all companies (96%) now have a formal strategy for how they will work with data, but only 38% think they’re doing a good job using data in their applications.

Building Scalable Streaming Applications with DataStax Astra Streaming

Wed, 09 Feb 2022 19:09:28 +0000

This post introduces developers to DataStax Astra Streaming with a step-by-step tutorial that illustrates how it can be used to easily build scalable, streaming applications.{#39ae}

Astra Streaming enables developers to build streaming applications on top of an elastically scalable, multi-cloud messaging and event streaming platform powered by Apache Pulsar . DataStax Astra Streaming is currently in beta, and we’ll be releasing a full demo soon. In the meantime, this article will walk you through a short demo that will provide a great starting point for familiarizing yourself with this powerful new streaming service.{#a6cf}

Four Reasons Why Apache Pulsar is Essential to the Modern Data Stack

Tue, 01 Feb 2022 22:14:58 +0000

Messaging has been on DataStax’s radar for several years. A significant motivator for this is the increasing popularity of microservices-based architectures. Briefly, microservices architectures use a message bus to decouple communication between services and to simplify replay, error handling, and load spikes.

With Apache Cassandra™ and DataStax Astra Cassandra-as-a-service, developers and architects have a database ecosystem that is:

Based on open source
Well-suited for hybrid- and multi-cloud deployments
Available in a cloud-native, consumption-priced service

There is no current messaging solution that satisfies these requirements, so we’re building one. We started by evaluating the most popular option, Apache Kafka. We found that it came up short in four areas:

Simplify Migrating from Kafka to Pulsar with Kafka Connect Support

Wed, 05 Jan 2022 11:30:09 +0000

Large-scale implementations of any system, such as the event-streaming platform Apache Kafka, often involve customizations and tools and plugins developed in-house. When it’s time to transition from one system to another, the task can become complicated, drawn-out, and error-prone. Often the benefits of an alternative system (which can include significant cost savings and other efficiencies) are outweighed by the risks and costs of migration. As a result, an organization can end up locked into a suboptimal situation, footing a bigger bill than necessary and missing out on modern features that help move the business forward faster.

7 Reasons to Choose Apache Pulsar over Apache Kafka

Wed, 15 Dec 2021 10:03:13 +0000

I wrote an earlier version of this article in 2019, while I was CEO of Kesque, a real-time messaging service built on Apache Pulsar , the cloud-native distributed messaging and streaming platform. A lot of big changes have happened in the interim; perhaps the most significant of these is the fact that the company I founded in early 2019 was acquired , in January, by DataStax. One thing that hasn’t changed, however, is the rationale behind our choice of Apache Pulsar.

Fast JMS for Apache Pulsar: Blazing Performance

Fri, 10 Dec 2021 08:54:44 +0000

DataStax recently announced the availability of Fast JMS for Apache Pulsar , a JMS 2.0 API. By combining the industry-standard Java Messaging Service (JMS) API with the cloud-native and horizontally scalable Apache Pulsar™ streaming platform, DataStax is providing a powerful way to modernize your JMS infrastructure, improve performance, and reduce costs. Fast JMS is open source and is included in DataStax’s Luna Streaming Enterprise support of Apache Pulsar.

What is JMS?

Java Message Service (JMS), or Jakarta Messaging as it is now known, is a standard messaging API that is part of the Java Enterprise Edition (now Jakarta EE ) ecosystem. It defines how to create, send, receive, and read messages between loosely coupled producers and consumers.

Enterprise-Level Apache Cassandra Sink Connector for Apache Pulsar

Wed, 24 Nov 2021 19:16:16 +0000

When DataStax started investing in streaming with Apache Pulsar ™, we knew that one of the first things people would want to do was connect existing enterprise data sources to Apache Cassandra™ using Pulsar.

Apache Pulsar has a powerful framework called Pulsar IO to enable this kind of use case, and at DataStax we already had a best-in-class Kafka Connect Sink that enables you to store structured data coming from one or more Kafka topics into DataStax Enterprise, Apache Cassandra, and Astra.

Apache Cassandra 4.0: Taming Tail Latencies with Java 16 ZGC

Tue, 22 Jun 2021 07:45:40 +0000

With Apache Cassandra 4.0, you not only get the direct improvements to performance added by the Apache Cassandra committers, you also unlock the ability to take advantage of seven years of improvements in the JVM itself. This article focuses on improvements in Java garbage collection that Cassandra 4.0 coupled with Java 16 offers over Cassandra 3.11 on Java 8.

Like so many others in the Apache Cassandra community, I’m extremely excited to see that the 4.0 release is finally here. There are many, many improvements to Cassandra 4.0 . One enhancement that is more important than it might look is the addition of support for Java versions 9 and up. This was not trivial, because Java 9 made changes to some internal APIs that the most performance-oriented Java projects like Cassandra relied on (you can read more about this here ).