The amount of data being produced is already incredibly great, and current developments suggest that this rate will only increase in the near future (Pence, 2014). A typical business problem addressed by Big data analytics is the ability to gain business insight. As data collected grows and becomes more diverse, there is a challenge for organizations to find meaningful insights. Big data helps to solve this challenge by providing significant insights, thereby promoting effective decision-making. Big data also helps solve challenges associated with risk management, brand management, optimized customer experience and service, and enhanced security capabilities.
We are not about to witness the end of data warehousing. This is because data warehousing is helpful for Big Data. Data warehousing and Relational database management systems (RDBMS) bring many powers that make them valuable for business intelligence. Some of these benefits c
Data for a business is considered its base of all activities. Businesses deal with huge data sets for their decision-making procedure to get aware of the opportunities that lie within. However, these companies also tend to face several challenges while managing those big data analytics. Handling big data might seem easy, but it is difficult (Lv et al., 2017). It isn’t very clear, and when the sources are scattered, it becomes even more complex. Companies deal with disadvantages associated with big data analytics like lack of skilled professionals, failure to understand huge datasets, multiple data sources and lack of synchronization among them, the problem with data expansion, the realization of the right tool for the data, and lastly, data security.
These challenges require addressing, which might make the tasks easier for the companies. Getting relevant information from the retrieved data is already a difficult job and having huge data sets from multiple sources adds to the difficulty. They must train the hired candidates about the use of tools and ways to improve productivity. Workshops might help employees understand big data analytics as that would serve them with the right knowledge. Using correct tools provides an added advantage to the skills utilized. Therefore, teaching the right measures within the employees on tools is essential. Finally, with growing data comes the risk of losing it. They must be prepared with the security techniques to ensure the safety of the data.
There was indeed a buzz about data warehousing getting vanished from the market, which was proven a wrong statement by several articles. It has been noticed that companies are still relying on data warehousing techniques for their data and comparing those data with previous records. It is a reliable source for the companies when they use data warehousing (Kimball, & R, 2008). However, the rumor was that the firms are now avoiding the data warehouse, as they are inclined towards big data solutions. One needs to understand that the two topics are in no way similar to each other. Both have a different purpose; one is used for solving and the other for relevancy.
Therefore, data warehousing is not witnessing an end due to the evolution of big data. Firms require warehousing techniques to support their decisions.
- PaaS solutions
Platform as a service is a new model for providing a platform in which developers build, test, develop and deploy applications that are available to any host, anywhere. Platform as a service is an integrated software platform used by cloud data centers and other applications for hosting and running applications (Suryateja, 2018). A company is using the Platform as a Service framework as an open application architecture, which can be scaled to any environment by any team. The platform-as-a-service framework for deploying a cloud application is a new technology to help organizations to create applications to run on any platform at any time (Suryateja, 2018)
Benefits of PaaS solutions.
The benefits of PaaS are:
- These cloud consumers can benefit from scalability and economic purpose since the consumer do not need to purchase hardware or pay expenses during the downtime.
- The consumers can focus on the development and management of their own services and save time setting up or maintaining core stack.
- It helps in speeding up with the creation of apps.
- It provides confidence to the consumers with respect to security expertise.
- Its dynamic nature allows provider to add capacity in peak time and reverse them as needed.
Define and describe SSO
A single sign-on (SSO) is an authentication method that verifies users’ identities. The primary goal of SSO is to improve the security of users in a business environment. With a single sign-on, a business can easily manage authentication and authorization because a single sign-on allows centralized management of rules or policies that govern authentication. (Convery, S. 2012).
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