Distributed Computing Basics for Big Data

If your company is considering a big data project, it’s important that you understand some distributed computing basics first. There isn’t a single distributed computing model because computing resources can be distributed in many ways.

For example, you can distribute a set of programs on the same physical server and use messaging services to enable them to communicate and pass information. It is also possible to have many different systems or servers, each with its own memory, that can work together to solve one problem.

Why distributed computing is needed for big data

Not all problems require distributed computing. If a big time constraint doesn’t exist, complex processing can done via a specialized service remotely. When companies needed to do complex data analysis, IT would move data to an external service or entity where lots of spare resources were available for processing.

It wasn’t that companies wanted to wait to get the results they needed; it just wasn’t economically feasible to buy enough computing resources to handle these emerging requirements. In many situations, organizations would capture only selections of data rather than try to capture all the data because of costs. Analysts wanted all the data but had to settle for snapshots, hoping to capture the right data at the right time.

Key hardware and software breakthroughs revolutionized the data management industry. First, innovation and demand increased the power and decreased the price of hardware. New software emerged that understood how to take advantage of this hardware by automating processes like load balancing and optimization across a huge cluster of nodes.

The software included built-in rules that understood that certain workloads required a certain performance level. The software treated all the nodes as though they were simply one big pool of computing, storage, and networking assets, and moved processes to another node without interruption if a node failed, using the technology of virtualization.

The changing economics of computing and big data

Fast-forward and a lot has changed. Over the last several years, the cost to purchase computing and storage resources has decreased dramatically. Aided by virtualization, commodity servers that could be clustered and blades that could be networked in a rack changed the economics of computing. This change coincided with innovation in software automation solutions that dramatically improved the manageability of these systems.

The capability to leverage distributed computing and parallel processing techniques dramatically transformed the landscape and dramatically reduce latency. There are special cases, such as High Frequency Trading (HFT), in which low latency can only be achieved by physically locating servers in a single location.

The problem with latency for big data

One of the perennial problems with managing data — especially large quantities of data — has been the impact of latency. Latency is the delay within a system based on delays in execution of a task. Latency is an issue in every aspect of computing, including communications, data management, system performance, and more.

If you have ever used a wireless phone, you have experienced latency firsthand. It is the delay in the transmissions between you and your caller. At times, latency has little impact on customer satisfaction, such as if companies need to analyze results behind the scenes to plan for a new product release. This probably doesn’t require instant response or access.

However, the closer that response is to a customer at the time of decision, the more that latency matters.

Distributed computing and parallel processing techniques can make a significant difference in the latency experienced by customers, suppliers, and partners. Many big data applications are dependent on low latency because of the big data requirements for speed and the volume and variety of the data.

It may not be possible to construct a big data application in a high latency environment if high performance is needed. The need to verify the data in near real time can also be impacted by latency. When you are dealing with real-time data, a high level of latency means the difference between success and failure.

Big data demand meets solutions

The growth of the Internet as a platform for everything from commerce to medicine transformed the demand for a new generation of data management. In the late 1990s, engine and Internet companies like Google, Yahoo!, and Amazon.com were able to expand their business models, leveraging inexpensive hardware for computing and storage.

Next, these companies needed a new generation of software technologies that would allow them to monetize the huge amounts of data they were capturing from customers. These companies could not wait for results of analytic processing. They needed the capability to process and analyze this data in near real time.

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