What Is Cloud Data Services (CDS)?

Data commonly stored in databases, like SQL or NoSQL database, but there are other types of data like the data we talk about in the context of Big Data. The moment we talked about Big Data, data analysis in the context of data becoming obvious. Of course the logs of web servers are also source of data and they need analysis. For the larger enterprises, sources of data are diverse and their analysis may discover a flaw or opportunity in some part. In this article, we will briefly discuss what is cloud data services in easy to understand language to the most as an introductory article.

What Are Cloud Data Services (CDS)?

Cloud Data Services (CDS) is a phrase to include a service catalogue or portfolio of services for data which includes services for all types of digital data, needed backend as a service for working with the data and all possible set of services from a specific vendor which they can provide for managing and analyzing dataset. They are commonly needed by a business, or a developer or a webmaster in situations when they either need a reliable backend as cost effective service or want to perform the analysis of data. Depending on the type of user or segment, the need of using a particular type of service from a third party vendor can vary. The real service from the Cloud Data Services portfolio for a particular need can be delivered as an easy to use Software as a Service (SaaS) as Web GUI on browser or as an expert advisory service for handling on-Premise data or simply as an optimized SQL or NoSQL database as a service for a running web software or developer or to analyze, predict from Big Data.

What Is The Need Of Cloud Data Services?

In a primitive way we can install ElasticSearch, Grafana, Apache Hadoop for web server’s log analysis on a dedicated or a virtual server. But such setup really can not even preserve one year’s log of MySQL database of a website with 3 00 000 visitors per month in a cost effective manner on a dedicated server. The amount of log files saved will be enormous by volume and it will be near impractical to interpret them in long run. Indeed, by default MySQL full logging is officially kept off in order to avoid servers getting flooded with huge size of log file. So, space is a matter to save such data in long run. In the past, an average user had some missing part as they cannot analyze these long term accumulated logs.
An individual webmaster to a medium sized enterprise never needed to know the basics of digital data. Simply, because there was not many ways to analyze such many types of data in a cost effective manner or it was just unnecessary. At least a decade back, the systems which needed to be accessible on public internet such as forums, various CMS, web applications used to store data in databases in the form of SQL or NoSQL databases on the same server. With increasing traffic on internet, some websites with higher traffic needed their database to be on a different server. Mainly for them, a subset of data services in the form of Database as a Service started to appear in the service catalogues of some standard vendors. Optimizing database themselves started to became unnecessary. Slowly, many F/OSS softwares including of Big Data analysis became available which decreased the need of using basic website analytics tools like Google Analytics with limited functions. At this moment, for a medium website we are talking about installing ElasticSearch, Apache Hadoop for web server’s log analysis. Data analysis scenario is rapidly changing.

With the penetration of IoT and related Smart Objects, data sources which really can be a source of analysis increased to many folds. Of course there are various sources of Big Data and need of having Data Lakes.

It is usually cost effective to use third party service compared to handling so much big size of data at own IT departments or by a webmaster or developer on their servers. Particularly for the later, it is easy to use a ready to use database as a service backend. Additionally, some third party service may offer machine learning based automation integrated with analytics. Such services can simplify and automate the ability to find, refine data instantly available for analytics or simply make the mobile and web applications better performing while enjoying the inherited properties of cloud computing — a on- demand self-service, opportunity of resource pooling, elasticity, measured service and other features like near instant replication to other data centers, backup and storage with a pricing model which is same like that of other cloud computing products. Also the users can associate their applications with the service using Restful API.

Cloud Data Services Is A Businesses Need

In order to scale and offload workload, a webmaster or application developer can use the optimized database backend part from the service catalogue of the Cloud Data Services or the developers can develop cloud based applications faster.

Cloud Data Services includes optimized services for managing and quickly interpreting the unstructured data needed for business innovation and decision making. The services usually equipped with cognitive intelligence, have data migration tools for the their platforms and their network is designed to minimize any downtime during upload of data. Altogether, these services can help in predictions to meet the business goals, can assist in decision optimizations, prevent fraud in real-time, perform network analysis and so on. The usage of the services will be suitable to variety of industries – manufacturing, utilities, finance, retail to web industry, developers and the data scientists.
An online flower retailer have lot of measurable parameters which positively and negatively affects their business. It is actually possible for them to process and analyze their already known data without having own IT department or having almost no working knowledge on data analysis only if they have a freelancer professional who can set the platform for them with the third party vendor.

From our this article, it is obvious that in any Cloud Data Services portfolio, we can expect two distinct parts – one set is towards the database services, and another set is for the analytics. Most of the readers who are used with our guides already read the guides on installing the Big Data tools (which are mostly Free Softwares from Apache Foundation). Essentially that backend is the basic technical background at lowest end of cloud data services and obviously it is not exactly cost effective when the amount of data is huge in amount or investment of purchasing hardware comes into question. Also, not all the softwares to make the things easy are not available as free software.