Wednesday, August 30, 2017

4 tips for managing big data from IoT in your network

4 tips for managing big data from IoT in your network
// Cumulus Networks Blog

Big Data and the Internet of Things. The two seem to go hand in hand, even if there are some important differences between them. As IoT becomes a greater reality, it's important that your network devops team is ready for its huge impact on your systems and networks. In this post, we'll cover the basics, like the difference between big data and the Internet of Things, and then we'll go into more detail about how to ensure your network is managing big data from IoT effectively.

The Internet of Things: a hot topic

The Internet of Things has been a hot topic in recent years. Little wonder, since its potential is increasing daily. From Bluetooth accessible devices such as smart appliances and smart homes, to wearable technology, to smart cars, to energy plants and wind turbines, smart technology is growing fast. Along with this technology is the need to support these devices both in network and storage. By 2025 McKinsey expects IoT will generate $11.1 Trillion annually. Companies are rushing to find ways to capitalize on IoT and the big data it will generate.

Differences between the Internet of Things and big data

Big Data is an interesting concept among engineers. It has many definitions, but is generally defined as a very large data set that can be analyzed and used to spot trends. The data is collected through different means such as manually by human beings, computational analysis, automatic data gathering and devices that fall into the realm of the Internet of Things. The Internet of Things, or IoT, deals with smart objects providing data in real time. So while IoT data may be big data, big data is not necessarily encompassed by IoT.

The network is crucial: managing big data from IoT

Due to the large amounts of data being aggregated by IoT technology, the management of big data in the data center has become more critical as the network is responsible for processing and transferring data rapidly. Because of this, it's imperative to have a network infrastructure that is flexible, agile and efficient. Big data processing requires a scalable, fault-tolerant, distributed storage system to work closely with MapReduce on commodity servers.

Many organizations look to clustering applications, like Hadoop, to manage their data efficiently. Manageability has become a key expectation from Hadoop users. Hadoop has made great advances with Ambari, which enables the automation of initial installation, rolling upgrades without service disruption, high availability and disaster recovery — all of which are critical to efficient IT operations with data management.

When you're thinking about designing a network that can support data clustering applications and process big data from the Internet of Things, it's important to consider how this data will grow and amplify as your business — and the amount of data you can gather — evolves. Here are a few considerations for optimal big data processing:

  1. Consider a non-blocking, multi-tier, scale-out IP Clos fabric design. Ensuring you have an elegant network structure to create redundancies and remove bottlenecks will lessen your risk of slower processing, outages and performance issues.
  2. Ensure you have a high-bandwidth infrastructure for rapid processing of large data so that as you are able to collect more data, your network infrastructure can grow with it.
  3. Be aware of bottlenecking. With hyper converged infrastructure, there is increased east-west traffic. Open networking is optimal for providing minimal bottlenecking.
  4. Thoroughly think through and diagram your architecture design with Hadoop thoroughly before you deploy a refresh or new private cloud environment. Hadoop works seamlessly with Cumulus Linux, and we have put together a comprehensive design guide to get you started.

Still interested in finding out more about big data? Check out our solutions page to learn about how Cumulus Linux works with big data.


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