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

Big Data and Cloud Native are currently the two major technologies that IT professionals are most concerned about. Cloud-native is about architecture, whereas big data is about managing enormous data. The fundamental driver of big data and Cloud technology's widespread industry acceptance, nevertheless, is the simplicity they deliver. The organizations benefit from the confluence of the two.

Big data and cloud-native technologies are discussed in this blog. We also discuss how important cloud native is too big data.

So, let’s start with it!

What Is Cloud-Native?

A method for creating tasks designed in the cloud and fully utilizing the cloud integration services model to use cloud-native infrastructure and technology.

Cloud-native emphasizes quickness and flexibility. The corporate industry is changing from being solutions for empowering capabilities to bringing solutions for strategic transformation that increase the speed and evolution of businesses.

There are three types of cloud native:

1. Public Cloud

Cloud-native that is pooled across enterprises and provided via the internet is known as public cloud. The public cloud, the most widely used type of cloud-native services, offers a wide range of options for enterprise mobility solutions and processing resources to meet the expanding needs of businesses of all sizes and industries.

2. Private Cloud

Any cloud service intended solely for use by one business is referred to as a private cloud. You don't share cloud-native resources with any other businesses when using the private cloud.

The data center resources might be found there or run off-site by a different vendor. The computing resources are not pooled between other clients and are provided via an encrypted connection network.

Private clouds can be tailored to a company's specific business and security requirements.

3. Hybrid Cloud

Any cloud technology architecture incorporating public and private cloud services is referred to as a hybrid cloud.

Usually, the services are coordinated as part of an integrated infrastructure environment. Depending on the organizational business and technological needs, apps and data workflows can divide the resources among public and private cloud deployment.

As we’ve checked cloud-native, let’s discuss.

What Is Big Data?

Data is all around us, and big data is the collection of all data. It encompasses data that an organization collects throughout its operational life, whether fully structured, moderately structured, or unstructured.

Because the dataset is frequently so large, traditional data analysis software cannot handle it. As a result, sophisticated tools and methods are needed to extract value from Big Data.

It creates datasets that are then further processed, analyzed, and managed to extract insights and outcomes from datasets; big data technology serves as the basis for data analysis.

Big Data Contains Several Essential Components That Must Be Considered

Volume: It is the term used to describe the quantity of data that is accumulating and offers increasing difficulty for its transfer, storage, and analysis. Usually, this would be several terabytes or more.

Velocity: It is the term used to describe the rate at which new data is produced and the pace at which data is transferred. Many firms are now able to gather data at an extraordinary pace. Data is being generated and sent very quickly by self-driving cars, smart meters, smart home appliances, industrial sensors, and other devices.

Variety: It includes both organized (such as user profiles or sales figures) and unorganized (like social media posts, emails, voice messages, videos, etc.).

Veracity: The origin or dependability of the data source, its setting, and the significance of the data to the business are all considered. Big data makes it harder to manage quality and accuracy.

Value: The capacity to transform data into a useful asset is referred to as value. Businesses must present a case before attempting to gather and use big data.

As we’ve discussed an overview of both, now, let’s move forward and discuss.

The Connection Between Both the Technologies: Big Data and Cloud-Native 

Digital transformation services are performing the sophisticated and large-scale computation in cloud-native. It clears the requirement to manage costly computing hardware, specialized storage, and software. Cloud-native has led to a tremendous increase in the volume of data, or big data, generated. In order to successfully handle and analyze big data, which is a difficult and time-consuming operation, a sizable computer infrastructure is needed.

In contrast to cloud-native, which is the digitalization of hardware resources, big data can be thought of as the incredibly efficient processing of enormous amounts of data. According to a future development trend, cloud-native will work as the base layer of computing resources to assist big data processing in the top layer. Big data will concentrate more on improving data analysis capabilities and real-time interactive query performance in the future.

Role of Cloud-Native in Managing Big Data Applications

Businesses need an effective means to maintain the increasing amounts of data they are collecting, and one of the newest best practices in online management systems is to save data on the cloud.

Big Data is comprehensive data that has been gathered from extensive network-based systems. This data is analyzed and accessed via the cloud, typically using a software as a service (SaaS) paradigm and leveraging AI and machine learning to provide information to users.

Big Data and Cloud Data work in tandem because the Cloud architecture makes it possible to store data, analyze it in real time, and analyze it fast and in bulk. The ability to scale is the main advantage of adopting cloud storage for large data because it can be used on a pay-as-you-go basis. In essence, the cloud is the system that provides, maintains, and offers users the chance to access and analyze Big Data efficiently. Data storage, organization, and analysis are challenges.

Cloud-Native and Big Data Work Together to Find Viable Alternatives

1. Scalability

A traditional business data center needs more room, power, cooling, or money to buy and install the massive amount of hardware necessary to create a big data infrastructure. In contrast, a public cloud oversees thousands of machines dispersed worldwide across a network of data centers. Users can make the architecture for a big data project of nearly any size because the hardware and software services are already available.

2. Storage

The storage of huge amounts of data is one of the main issues. Physical infrastructure is insufficient to handle this enormous volume of data appropriately. Even if capacity is not a problem, customers may still need help due to the physical storage's scalability.

Cloud-native offers dependable, safe, and flexible storage facilities to store and retrieve huge amounts of data. Because of the decentralization and elimination of physical infrastructure, these remote storages relieve users of maintenance duties.

Scalability is not a problem because cloud storage services are based on a pay-as-you-go basis, and this storage may be readily raised or lowered as per users' needs.

3. Analysis

Big Data analysis has become better thanks to the development of cloud technologies, producing superior results. Numerous cloud-based storage alternatives have built-in cloud analytics for a thorough insight into your data. You can quickly implement monitoring systems and make custom reports to evaluate data across your entire organization when your data is in the cloud.

You can increase productivity and develop courses of action to achieve company objectives based on these results. Because of this, companies decide to perform Big Data analysis in the cloud. The integration of data from various sources is made easier by the cloud.

4. Cost

A company's data center is a significant capital expenditure. Businesses must spend money on expenses such as premises, power, routine maintenance, and more, in addition to the hardware. The cloud accounts for all those expenses under a dynamic renting model where resources and services are pay-per-use and offered on demand.

5. Minimize Complexity

Any big data solution installation needs a number of parts and integrations. By offering the ability to automate these elements, cloud-native lowers complexity and boosts the overall performance of the team responsible for extensive data analysis.

6. Elasticity

A cloud platform can seamlessly extend its storage capacity to accommodate ever-growing data. Storage capacity can be raised or decreased to handle the data as needed once the firm has obtained the appropriate information from the data.

Conclusion

Cloud adoption is growing as businesses innovate and scale out with Big Data efforts. On the other hand, people solely concentrate on gathering data because they can use cloud-native to examine the data and provide economic value. Big data and cloud-native both have a significant impact on modern life. 

A corporation may learn how to enhance current business processes in order to benefit by merging these two approaches. Additionally, it can assist any organization in exploiting rivals and ensuring sustainability. Lesser obstacles made it possible for firms to access data and unlock disruptive innovation thanks to the pay-as-you-go cloud-native model.