Big Data sustainability is well known as a great difficulty – you can only dump a certain amount of hardware at an issue before it becomes overwhelming for the software to control or manage it all due to poor architecture.
As DevOps approaches have been increasingly popular in recent years, a lesser-known idea called microservices has also gained traction. The microservices architecture concept aims to create your application as a collection of discrete services rather than a single huge codebase, known as a monolith. Rather than using huge databases to access a bulk of your data, many handle networking through API calls between systems, with each operation having its separate lightweight database.
Numerous best practices can allow greater integration across many aspects of software development lifecycles. However, microservices architecture does not necessarily demand more than the key notions described above. Containerization, seamless integration, automated integration testing, DevOps, and Agile development are the most common microservices methods.
This article will explain the benefits of using data analytics with microservices. We will also shed light on the challenges of implementing a microservices architecture.
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Benefits of Using Microservices for Data Analytics Applications
Microservices can assist enterprises in overcoming some of the challenges they confront. Here are some of the most pressing concerns about Big Data programs and how microservices architecture might help.
- Accessibility and Flexibility of Applications
Flexibility and accessibility are among the main advantages of using data analytics with microservices architecture for Big Data programs. While not equivalent to cloud computing, we can interchangeably use the two frequently. Traditional monolithic apps lack the flexibility of microservices-based solutions. Because each service runs on its own server, its resources may be scaled up and down as needed. - Team Placement and Recruitment
As described before in this blog, Microservices enable you to use different technology frameworks throughout your services. It also helps you to employ and manage your software engineering teams appropriately.
There are several methods to design in terms of how work is split among teams, although cross-functional groups are more popular in microservices architecture systems. Because the functions are not as complicated as a monolithic application, they are considerably easier to comprehend, making it simpler for developers unfamiliar with the code – or the software language – to include features or make changes.
The flexibility to leverage several coding languages and technological frameworks also considerably expands the skill pool of possible hires. Of course, you do not want your software platforms to be too fragmented, so most businesses stick to two or three major programming languages and related technologies. - Enhanced Data Safety and Quality
Many breakdown sources can emerge as data transmits through the intake process, and data accuracy is one of the most significant difficulties connected with Big Data programs. Microservices can aid with data quality, a multi-faceted challenge with numerous functional components. The software development teams responsible for generating microservices have a considerably narrower focus. It is easy to design, manage, and test systems as they move through the application’s ingestion and modification process since each system has a specific job.
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Best Practices for Using Microservices for Data Analytics Applications
Microservices may provide various benefits to your project’s data analytics applications when correctly built in conjunction with certain best practices, including:
- Simpler Implementation Procedure
The complete application does not require to be re-deployed when updating a single feature; only the service that needs to be updated does. - More Accurate Failure Monitoring
It is easy to analyze and detect whether one of the systems has a problem with microservices. Of course, this involves the use of appropriate software to monitor those services. - Using Different Technological Frameworks and Programming Languages
You may utilize diverse technology frameworks throughout your microservices — wish to deploy Java or Scala, we will tell you to just do it! Microservices have you covered! Additionally, microservices have turned these tasks into a much easier form compared to a monolithic application, so it is quite easy to move to alternative technology frameworks. - Improved Implementations and Seamless Integration
Distributing the application over many code repositories provides an easy way for developers to publish updates to their microservices regularly and automated testing.
Technologies for Using Data Analytics With Microservices Successfully
There are also a few Big Data-related technologies that function excellently with microservices architecture:
- Communications Methods
Microservices must use lightweight messaging to succeed, especially given the large number of API calls they must handle. Apache Kafka, Rabbit MQ, and Amazon Kinesis are all popular systems. These systems are frequently integrated with Big Data programs, even though they are not inherently Big Data technologies. - Big Data and Cloud Computing
Amazon’s AWS and Microsoft’s Azure are at the forefront of cloud computing solutions-oriented at Big Data programs, enabling simplicity of implementation and resource scalability – both of which are required for microservices to reach their full potential. - Orchestration Technologies
Workloads are distributed across the system using platforms like Kubernetes and Apache Mesos. These applications are perfect for distributed computing systems like Apache Hadoop.
Technologies for an Efficient Microservices Architecture
A few platforms and frameworks contribute to the success of microservices architecture. Here is a handful of the most important:
- Systems for Containerization
Switching between creating features on several microservices remotely would be a pain to set up. Systems like VMware vSphere and Docker enable you to install images on the computer directly, independent of the operating system. - Technologies for Automated Integration Assessment
There are far too many integration testing suites to include here, while Selenium is likely one of the most popular. These platforms are critical in Continuous Deployment and microservices architecture and any other application. - Tools for Implementation Automation
Automation tools like Jenkins, Puppet, and Chef work well with microservice architecture, enabling genuine continuous development and activation.
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Challenges of Microservices Architecture Implementation
There are certain limitations to using microservices.
Additional Personnel Required
The increased complexity of using microservices in conjunction with Continuous Integration necessitates some significant DevOps skills, which may be difficult to come by – especially given how difficult it is to recruit top Big Data talent. Furthermore, we have been creating monolithic apps for so long that not everybody is acquainted with the design, thus complicating the problem of trying to introduce microservices in a current monolith.
Increased Project Difficulty
To avoid building duplicating services, a lot of cross-team communication is essential. We must also ensure that data types are kept consistently throughout services. This problem is particularly difficult to solve because teams operate on separate code sources. Especially with bigger systems, this will typically have limited awareness of the other created systems.
Conclusion
Although microservices are difficult to develop, many believe the advantages justify the added expense and application complexity. If you are embarking on a new Big Data program, you should at the very least think about examining it as a possible alternative.
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