Back to: Data Science Tutorials
Big Data Distributed Computing and Complexity
In this article, I am going to discuss Big Data Distributed Computing and Complexity. Please read our previous article, where we discussed Big Data Challenges and Requirements. At the end of this article, you will understand everything about Big Data Distributed Computing and Complexity.
Big Data Distributed Computing and Complexity
Big Data Analytics is a reality because of distributed computing, management, and parallel processing principles, which allow for the acquisition and analysis of intelligence from large amounts of data. Different parts of the distributed computing paradigm address various types of Big Data analytics difficulties.
If your firm is considering a big data project, you should first learn some distributed computing fundamentals. Because computer resources can be spread in a variety of ways, there is no single distributed computing model.
You can, for example, distribute a collection of programs on the same physical server and utilize messaging services to allow them to communicate and send information between them. It’s also feasible to combine several distinct systems or servers, each with its own memory, to address the same problem.
In recent years, the scale and complexity of distributed computing systems have grown inexorably. Internet systems, ubiquitous computing environments, grid systems, storage systems, business systems, and sensor networks are all examples of large-scale distributed systems with a large number of heterogeneous and mobile nodes. These systems are extremely dynamic and prone to failure. As a result, developers struggle to create new applications and services for these systems, administrators struggle to maintain and configure these complicated, device-rich systems, and end-users struggle to use these systems to accomplish tasks.
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 be 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 have 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 reduced 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 the 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 first-hand. 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 the decision, the more that latency matters.
In the next article, I am going to discuss What is Hadoop. Here, in this article, I try to explain Big Data Distributed Computing and Complexity and I hope you enjoy this Big Data Distributed Computing and Complexity article.