The term clustering or cluster programming is used to refer to a group of servers that are connected via networks, software, and necessary hardware equipment to act as part of the same single system.
Clustering aims to make a group of computers look like they are one single entity to the rest of the world. You can learn cluster programming as part of any Data Science Training module at any certificate course.
With the evolution of big data and analytics, information is also constantly evolving into a more dynamic and abstract concept. To further learn cluster programming and its working as a computer code, you need to understand certain things about clustering:
Limits of parallel computation and Amdahl’s Law
In 1967, Gene Amdahl, a computer scientist, presented a paper to talk about parallel computation and the limits of parallel computers, at the AFIPS Spring Joint Computer Conference that year; this eventually was what came to be known as Amdahl’s Law. This law is used to describe the set limit of the maximum speedup that is achievable for a problem that is mixed, with components that are both serial and concurrent. This further explains how for a that is problem computational, parallel and concurrent upto 95 %, there is 5% remaining that is computed serially. In this case, the maximum speedup that can be reached is 20.
How to cluster computing is different from parallel, cloud, grid, distributed computing
It is essential to understand how cluster computing is different from all other kinds of computing. Distributed computing relies heavily on parallel computing however parallel computed doe not require a distributed computing for it to work. Parallel computing most commonly works in most desktop computers that are standardly available in the market with multicore processors.
The software is average and mostly written in programming languages that are multiparadigm and concurrent. Parallel computing works on sharing resources with and distributing them among a large number of computers, connected together in a network. The most basic difference between grid cluster and cloud computing lies in the fact which decides how these resources work together and how they fit in.
Why is Cluster Programming so important?
Cluster Programming deals with combining the resources from a distributed computer network to service a single task or a single user. Clusters are high utility owing to the following factors that only level up their importance in the world of data science.
Higher availability
The world of data science is vast, where core systems need to be functioning all the time and front-end web servers need to be running. In instances like this, high availability is a must-have. This is exactly where clustering fits in. It not only provides backup that is transparent in nature but also ensures high-speed delivery of systems, data, and peripherals.
Server Integration
While a server can be purchased to handle an organization’s sole and personal needs, however, all processing needs cannot be magically solved by one single computer in the cloud. Often, server resources need to be customized for certain applications to meet a company’s needs, which are:
- Web application servers
- Data transaction servers
- Appliance servers
Applications that work withing all these classes need to share and update real-time data o be in sync with each other. Functions are being centrally arranged instead of following the traditional model of being connected to individual servers which also means, scalability is soaring and working is getting more efficient. Cluster programming plays its role here in seamlessly bringing together all of these three servers along with making the network more centralized and storage of recourses more compact. Young enthusiasts can learn cluster programming by pursuing a course in data science with Spark.
