10 Advantages to Building Enterprise Applications with Microservices

1 - 10 Advantages to Building Enterprise Applications with Microservices
2 - They Promote Big Data Best Practices
3 - They Are Relatively Easy to Build and Maintain
4 - They Enable Higher-Quality Code
5 - They Simplify Cross-Team Coordination
6 - They Enable Real-Time Processing
7 - They Facilitate Rapid Growth
8 - They Enable More Outputs
9 - Easier to Assess Updates in the Application Life Cycle
10 - They Enable Scale
11 - Many Popular Tools Are Available
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10 Advantages to Building Enterprise Applications with Microservices

Microservices, single-purpose applications that can be assembled to build large-scale software systems, will be an important tool that enterprises use to modernize their application portfolios.

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They Promote Big Data Best Practices

Microservices naturally fit within a data pipeline-oriented architecture, which aligns with the way big data should be collected, ingested, processed and delivered. Each step in a data pipeline handles one small task in the form of a microservice.

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They Are Relatively Easy to Build and Maintain

Their single-purpose design means they can be built and maintained by smaller teams. Each team can be cross-functional while also specialize in a subset of the microservices in a solution.

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They Enable Higher-Quality Code

Modularizing an overall solution into discrete components helps application development teams focus on one small part at a time. This simplifies the overall coding and testing process.

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They Simplify Cross-Team Coordination

Unlike traditional service-oriented architectures (SOAs), which typically involve heavyweight inter-process communications protocols, microservices use event-streaming technologies to enable easier integration.

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They Enable Real-Time Processing

At the core of a microservices architecture is a publish-subscribe framework, enabling data processing in real time to deliver immediate output and insights.

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They Facilitate Rapid Growth

Microservices enable code and data reuse the modular architecture, making it easier to deploy more data-driven use cases and solutions for added business value.

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They Enable More Outputs

Data sets often are presented in different ways to different audiences; microservices simplify the way data can be extracted for various end users.

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Easier to Assess Updates in the Application Life Cycle

Advanced analytics environments, including those for machine learning, need ways to assess existing computational models against newly created models. A-B and multivariate testing in a microservices architecture enable users to validate their updated models.

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They Enable Scale

Scalability is about more than the ability to handle more volume. It's also about the effort involved. Microservices make it easier to identify scaling bottlenecks and then resolve those bottlenecks at a per-microservice level.

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Many Popular Tools Are Available

A variety of technologies in the big data world, including the open-source community, work well in a microservices architecture. Apache Hadoop, Apache Spark, NoSQL databases and many streaming analytics tools can be used for microservices.

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