Types of visualisations

 While the recent trend towards cloud computing might make it seem like virtualization is new, it has existed in some form for many decades.

As far back as IBM’s mainframe era in the 1960s, virtualization played a part in the way computing was handled to make it more efficient and reduce the load on the individual machine.


The 5 most popular types of virtualization are below.


1. Desktop: allows multiple virtual machines to run cloud based desktops.


2. Application: Creates
a virtual instance of the applications needed for core business operations, which keeps app software off of local OS


3.Server: Creates a virtual server in place of the physical one, allowing for management of the server through the cloud.


4. Storage: Stores the enterprise’s data in a secure cloud, removing the need for physical data storage and potentially reducing the costs associated with space in a data centre.


5.  Network: Uses physical and virtual components to create a hybrid network, allowing administration of the network through software switches.

 

Comments

Post a Comment

Popular posts from this blog

Strategies for limiting the negative effects of big data

Image
 A well-executed big data strategy can streamline operational costs, reduce time to market and enable new products. Below are listed some strategies that some organisations use. 1. Managing large volumes of data: Big data by itself definition typically involves large volumes of data. Once you have a sense of the data that's been collected it is easier to narrow in on insights by making small adjustments. 2. Finding and fixing quality issues:    The analytics algorithms and artificial intelligence applications built on big data can generate bad results when data quality issues creep into big data systems. These problems can become more significant and harder to audit as data management and analytics teams attempt to pull in more and different types of data 3. Dealing with data integration and preparation complexities:   Some enterprises use a data lake as a catch-all repository for sets of big data collected from diverse sources, without thinking through how the dispa...
Read more

Types of problem suited to big data analysis

Image
Generally, most organisations that deal with Big Data have several goals for Big Data projects. While the primary goal for most organisations is to enhance the experience for the customer, other goals are also considered. When considering Big Data organisations will consider a few things. 1. Trying to decide whether there is true value in Big Data or not. 2. Evaluating the size of the market opportunity. 3. Developing new services and products that will use Big Data 4. Already using Big Data solutions, Repositioning existing services and products to utilise big data 5. Already utilizing Big Data solutions
Read more