Viewpoint: The best choice of the open source software big Data age

Over the past 12 months, big data waves have swept across the globe. Even the largest institutions lack the infrastructure, tools, and methodologies that directly lead to a lack of ability to effectively extract critical data from large data and transform it into insights into business. But the world of big data is changing today. For all types and sizes of organizations, massive open source software and low-cost hardware combinations greatly reduce the threshold for large data processing systems.

Simply put, open source solutions allow organizations to scale clusters to thousands of servers in a short period of time to better support large data services. and simply pay the cost of the resources used. The following points also fully illustrate that open source software is the best choice for the big Data age.

1. Contains numerous large data-processing tools

Although proprietary operating systems are upgraded every 2-3 years, they provide a shorter release cycle and longer system support time than most open source systems for proprietary systems. This also means that all organizations are able to get the most appropriate large data processing tools from their business requirements (from Hadoop, Cassandra to MongoDB, Couchbase).

2. Better compatibility

Open source operating systems provide support for both public and private clouds and provide seamless migration of workloads between the public cloud and private cloud. Open source systems support real-time provisioning and scaling. The Nimbula Director Cloud operating system, which is compatible with Amazon EC2, is an excellent example. Nimbula provides the ability to manage both private and public clouds through its Nimbula Director service. Nimbula services allow the porting of existing private cloud applications to the public cloud, using APIs that can manage the entire cloud resource.

3. Rapid deployment of large data infrastructures

The infrastructure that handles large data services must be flexible and easy to deploy. So having the task of automatically installing an instance on a "bare metal" server and dynamically computing resource processing to cope with different workloads based on changing business requirements is a feature that large data software must have.

4. Service-oriented development

In the future, developers will be more concerned about services than the underlying infrastructure. Today, there are components that can be used to configure services and the underlying infrastructure, which makes the DevOps (DevOps) A collection of processes, methodologies, and systems for communication, collaboration, and integration between software development, operational and peacekeeping quality assurance three departments. Can deploy large data services, allocate related resources, and organically integrate all the necessary infrastructure and applications in minutes.

5. No Authorization restrictions

It is well known that large data processing systems require a large amount of infrastructure. Proprietary systems often require expensive licensing costs. Because each computer is not required to have the appropriate license, open source operating systems can significantly save money when deployed in large data environments.

6. Perfect Hardware Support

Free open source operating systems have now been proven to be the best choice for running on hardware in Low-cost, commercially available data centers. This key point is also one of the biggest benefits because large data requires a large amount of computational resources.

Open source technology is helping organizations of all types and sizes transform large datasets into meaningful business intelligence data. Open-source software is obviously much cheaper than the expensive proprietary systems deployed in large, distributed, large data environments. More importantly, open source software does not generate additional licensing fees when it needs to be expanded for large data processing systems.

For these reasons, open source software has become an important place in the field of large data applications. Most importantly, the large data-processing platform built by open source software has a good scalability in computing and storage, and can really effectively ensure that the organization gets the results they want most quickly. (Li/compiling)

(Responsible editor: The good of the Legacy)