Articles

Unlike analysts at the large firms, who have to specialize in narrow market segments to avoid stepping on each other’s toes, we at Intellyx have the luxury of covering cross-cutting topics that align with business needs.

One of our tools in trade: looking closely at how two different markets interrelate and thus provide business value. In today’s Cortex, I’ll consider the relationship between low-code and cloud-native computing.

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Integrating SAAS application workflow
The previous article in this series looked at a SaaS CRM connector integration example. The foundation for this logical diagram was researching a use case where customers were successful with a portfolio solution.

It’s a starting point for the generic architectural blueprint that rises from several customer solutions that were researched. Having completed the outline of the blueprint details and the resulting logical diagram elements, it’s now time to take a look as specific examples.

In this article, we’ll continue building the previous examples by sharing how customers are integrating with third-party platforms in their architectures as SaaS platforms.

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Quarkus supports imperative as well as reactive programming styles. In this article, I compare access times to Postgres from Java-based microservices developed with Quarkus. For synchronous invocations Panache is used, for asynchronous access Vert.x Axle.

I’ve created a sample application that comes with the cloud-native-starter project. The ‘articles’ microservice accesses the database running in Kubernetes. To keep the scenario simple, only one REST API is tested which reads articles from Postgres.

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Modern businesses are highly consumer-driven. Delivering value to our customers should, therefore, be our first priority. Making the tasks of our customers more convenient and efficient should be our primary goal. To do that, we need ways to figure out “what” exactly makes our customers more efficient and brings them convenience in their tasks. 

This requires a lot of trial and error. This requires us to build and experiment with systems and features to see if these capabilities actually bring significant value to our customers. This is the primary motivation that drives enterprise architecture to be much more disaggregated and composeable. Heard about “Microservices” anyone? 

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What’s this “Producer/Consumer” thing? It’s around us, everywhere. Every time you see some kind of workflow with multiple serial steps, that’s an example. A production line in a car factory, a fast-food kitchen, even the postal service.

So why do we care about it? Well, that’s easy: in almost every piece of software we write, there’s a pipeline to fulfill. And as every pipeline, once a step is completed the output is redirected to the next one in line, freeing up space for another execution.

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If you read the articles in the Microservices zone regularly, you are probably interested in not just the specifics of microservices, but also the efficiencies it brings.

With that in mind, we’d like to pick your brains for a few minutes.

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Introduction

File-based integration is one of the most popular and widely used approaches in data integration. Many software systems work with files — i.e., they either take in files as input, give their results as files, or do both — and working with multiple disparate systems requires connecting those systems together. Newer software systems also provide API-based access to information, and API-based integration is one of the approaches in connecting such systems. Although APIs provide advantages over traditional file-based systems, such as complex data structures and fine-grained security, many legacy software systems, and even modern ones, often use file-based approaches to export and import data. 

Integration requires ensuring that two separate systems understand each other. And, although file structures and data types will likely be different in different software systems, we need an integration mechanism to map, transform, filter, and cleanse data in order for software systems to understand each other. Some of the most popular file formats used in general scenarios are CSV, EDI, JSON, and XML.

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Learn how to develop a custom Spring Cloud Stream binder from scratch.

Spring Cloud Stream is a framework built on top of Spring Boot and Spring Integration that is designed to build event-driven microservices communicating via one or more shared messaging systems.

The core Spring Cloud Stream component is called Binder,” a crucial abstraction that’s already been implemented for the most common messaging systems (e.g. Apache Kafka, Kafka Streams, Google PubSub, RabbitMQ, Azure EventHub, and Azure ServiceBus).

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Use the Spring Cloud Load Balancer!

You may also like: Service Discovery and Client-Side Load Balancing With Eureka and Ribbon

Almost a year ago Spring Cloud has announced that most of Spring Cloud Netflix OSS projects will be moved to the maintenance mode starting from Spring Cloud Greenwich Release Train. The maintenance mode only does not include Eureka, which still will be supported. I referred to that information in one of my previous articles The Future of Spring Cloud Microservices After Netflix Era.

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It was the early 2000’s, your .NET application was the best thing to hit the streets since the IBOOK G3 came out. Let’s just say that your application was so money, it didn’t even know it. It had its shiny new (insert any sweet .NET functionality here) and all of the Java-based applications were jealous of it. Those were the days…

Now turn to today. You feel like John Ritter and your application is the problem child from hell. It’s stuck in the past; it won’t allow you to update it. You’re constantly supporting all of its bad consumption habits and it won’t play nice with your other applications.

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