Cloud Analytics, Testing under cloud

Cloud analytics is the process of storing and analyzing data in the cloud and using it to extract actionable business insights. Similar to on-premises data analytics, cloud analytics algorithms are applied to large data collections to identify patterns, predict future outcomes and produce other information useful to business decision makers.

However, cloud analytics is generally a more efficient alternative to on-premises analytics which requires businesses to purchase, house and maintain expensive data centers. While on-premises analytics solutions give companies internal control over data privacy and security, they are difficult and expensive to scale. Cloud analytics, on the other hand, benefits from the scalability, service models and cost savings of cloud computing.

Businesses generate terabytes of data in the course of daily operations. Today, most of this data sourced from websites, social media, IT devices and financial software, among other things exists in the cloud. Cloud analytics tools and analytics software are particularly efficient for processing these huge data sets, producing insights in easily digestible formats and creating insights from data in the cloud available on demand, resulting in a better and more streamlined user experience.

Cloud analytics tools and analytics software are particularly efficient for processing these huge data sets, producing insights in easily digestible formats on demand that result in a better and more streamlined user experience.

Cloud Analytics is particularly interesting for several reasons:

• The amount of data collected around the world is growing at staggering rates and much of it is being created and pooled in the cloud or at IOT endpoints.
• Services delivered in the cloud are much easier to deploy as they are delivered as an automated service and they don’t require deployment and maintenance of physical hardware.
• The cloud business model enables a user to turn services on and off as needed. This consumption approach allows customers to pay only for what they use when they use it, thereby removing the responsibility of procuring and managing capital infrastructure as well as reducing data center space.
• The cloud allows users to deploy the right quantity of IT resources to match the problem at hand. Dynamic resizing of resources means that users can easily apply compute and storage and scale them as needs change. Users are spared the requirement to procure a fixed capacity of physical IT equipment for all of their data analysis projects.
• Building a hybrid analytics solution is effective for users who wish to leverage the cloud to test a new analytics project as a POC before committing to investments on-premises.

Cloud Analytics empowers organizations to:

• Identify patterns in speech, images and videos in order to improve customer satisfaction and improve customer service.
• Test genomic data to better understand genetic disease and how to offer cures.
• Study buying behavior to improve product availability and delivery.
• Analyze hybrid cloud infrastructures to improve application performance and optimize IT costs.
• Identify patterns of disease reporting to improve availability of medicine and vaccines.

Testing under cloud

Cloud testing typically involves monitoring and reporting on real-world user traffic conditions as well as load balance and stress testing for a range of simulated usage conditions.

Load and performance testing conducted on the applications and services provided via cloud computing particularly the capability to access these services in order to ensure optimal performance and scalability under a wide variety of conditions.

Testing under the cloud gives very good sign by decreasing the manual intervention and reducing the processes in the typical testing environment.

After enabling of resources as and when they are required, it reduces the investment on capital as well as enables the business to handle the ups and downs of the testing requirements.

Advantages:

• Offers new and attractive services to the clients and present an opportunity to speed cycles of innovations and improve the solution quality.
• Reduces capital investment and operational costs and not effect goal critical production application.

Type of Testing in Cloud

The whole cloud testing is segmented into four main categories

Testing of the whole cloud: The cloud is viewed as a whole entity and based on its features testing is carried out. Cloud and SaaS vendors, as well as end users, are interested in carrying out this type of testing

Testing within a cloud: By checking each of its internal features, testing is carried out. Only cloud vendors can perform this type of testing

Testing across cloud: Testing is carried out on different types of cloud-like private, public and hybrid clouds

SaaS testing in cloud: Functional and non-functional testing is carried out on the basis of application requirements

SaaS Testing

SaaS Testing is a software testing process in which the software application built in a Software as a Service model is tested for the functional as well as non-functional requirements. The goal of SaaS testing is to ensure the quality by testing data security, integrity, performance, compatibility and scalability of the software application.