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Cloud Native Applications

Role of Cloud-Native in Managing Big Data Applications | Quick Guide

Gursimran Singh | 14 March 2023

What is Big Data in Cloud Computing?

The efficient operation of large data applications in cloud-native environments is a difficult problem with both theoretical and practical value.

As more and more people get friendly with the internet, more data is being added over the servers with time. Whatever a person is accessing on the internet or computer is data. Data can be in any form, i.e., text, image, video, or anything transmitted as an electric signal.

Data is heightened continuously with time, so for this scenario, a new term is introduced known as Big Data. It is the collection of data in vast amounts with high velocity and still growing with time. It is data that is so large and sophisticated that none of the standard data management tools can store or process it properly. Examples are below:

  • Data on social media(Facebook, Instagram, Whatsapp, etc)
  • Data on Stock exchange.
  • Data from hospital agency

Data from one TeraByte and above is considered as BigData.

A sort of database service which is used to build, deployed and delivered through cloud platforms. Click to explore about our, Cloud Native Databases on Docker

What are Cloud-Native Platforms?

It is a modern way of building and using software applications that use the flexibility and robustness of cloud computing. It combines a variety of tools and methods used by software developers today to build public cloud applications, as opposed to conventional structures suitable for a local data center. Some of the popular cloud-native platforms are Mesos, Kubernetes, and Openshift.

Major Components of a Cloud-native platform

Cloud-native is changing the usual way of software development. The software development process is so fast that the products are delivered quickly. It also provides easy-to-manage applications, as each application is treated as a microservice.

  • Microservices: Microservices are a way to develop a single application as a set of small services, each running through its process and communicating using lightweight protocols such as HTTP.
  • Containerization: Containers make it possible to classify applications into smaller, lightweight applications that share the operating system kernel. Generally measured in megabytes, containers use much fewer resources than virtual machines and start faster. Docker has become the standard of container technology. The most significant benefit they give is portability.
  • DevOps: DevOps is about culture, collaborative processes, and automation that aligns development and operational teams with having a shared focus on developing customer knowledge, responding quickly to business needs, and ensuring that innovation aligns with safety and performance requirements.
  • CI/CD: Continuous integration (CI) and continuous delivery (CD) is a set of operating systems that allow application development teams to deliver code changes consistently and reliably. CI technology aims to develop a consistent and automated way to build, package, and test applications. With the consistency of the integration process, teams are likely to make code changes over and over again, leading to better interaction with software quality.

In recent years, increasing AI and Big Data applications have been deployed via cloud-based orchestration frameworks such as Kubernetes. This is because the containers offer effective shipping and fast duplication, as well as exaggerating the natural benefits of cloud computing in terms of service costs and expandable scaling. However, before Fluid happened, the Cloud Native Computing Foundation (CNCF) site did not have a native component to help these applications that need data efficiently, securely, and easily access data in traditional cloud computing.

Applications that are developed and deployed using cloud-based technologies are known as Cloud-Native Applications. Click to explore about our, Why Cloud Native Applications?

Benefits of the Cloud-Native Model in Big Data applications

Although not directly linked, the benefits of integrating microservice architecture with Big Data applications are enormous.

  • Scalability: One of the greatest benefits of using the microservice architecture of its applications is the balance it offers. Although not quite like cloud computing, it is common for both to be used together. The traditional monolith app does not have the flexibility of applications built into microservices. With each stand-alone service, the servers can be up and down with resources as needed. This is especially important for its systems, which are usually utility pigs, as they handle data with high volume and speed.
  • Data Consistency and quality: Big Data increases the speed and volume of data processed simultaneously on a server. It also enhances the diversity and authenticity of uncertain data. As data volume grows, it is essential to monitor data quality. For example, the data error in the Nasdaq exchange has caused quite a stir recently, as the introduction of test data into live systems has significantly impacted several technology companies' stock prices. Two notable examples: Amazon prices dropped 87%, while Zynga rose 3,292%. In this case, the error may be directly related to the quality of the data.

Apps built into microservices are easier to maintain, test, and scale than monolithic applications.

  • Ease of code modification: Microservice frames allow different staff working with different coding languages ​​to modify codes. This will benefit your organization, especially by combining diversity and strengthening your collection of talents.
Compute the streaming data that is used for real-time analytics. Click to explore about our, Big Data Platform

Manage Big Data Applications in Cloud-Native environments

Let me underline some of the significant points which make its applications very annoying:

  • Heavy in size (Memory and CPU), some of its tools required colossal memory and CPU to run smoothly.
  • Many configurations: The bitter truth is that Big Data tools must be configured.
  • Tools do a lot of data processing every second, so they require high-quality disks.

When we run its tools as containers, it's easy to manage them. We can specify the CPU and memory limits for the tools to use resources efficiently. This means working with its tools could be costly if not used efficiently. However, microservice provides a way to use hardware and applications efficiently. If you use its environment, it will provide you with better:

  • Scalability
  • Cost management
  • Quality

Its environments provide applications to be deployed as microservices. Microservices are very much flexible to modularise the application and an easy way to connect different modules. Applications are efficiently handled in their environments. As mentioned above, It is easy to scale applications.

Switching between feature enhancements on many different microservices in the area can be a nightmare, thanks to platforms like Docker and VMware. vSphere allows you to use images on your computer locally, no matter what app you use.

Big Data tools with fluid

Fluid is a cloud-native infrastructure project. Driven by the split of computing and storage, it aims to deliver an efficient & convenient data abstraction for AI and cloud-native apps by abstracting data from storage to achieve benefits like data affinity scheduling and ensuring secure data isolation & distributed cache engine acceleration.

Fluid helps bridge the gap between it & Big Data tool processing frameworks designing concepts & mechanisms.

cloud-siem-managed_03 illustration Cloud-Native based SIEM Solutions
An approach that works on simplifying software and application development with continuous delivery and agility. Download to explore the potential of Cloud Native Applications.


In this blog, we have gone through the overview of its tools and the cloud-native approach by having insights into the benefits and challenges faced when Big Data tools adapt microservice architecture. Though implementation of microservices is challenging, considerably would have consented that the advantages overpower the additional cost & application complexity. At the very least, if you're beginning a new Big Data endeavor, it would be worth your time to analyze it as a viable option per your use case.