Big Data

The rapid increase in quantity of data from various sources is what Big Data is focused on and it can lead to the development of new technologies and tools.The large amount of data collection is reaching a tipping point for major technological changes that can bring new ways in decision making, managing economic growth. Big Data Analytics has the challenge to design highly scalable algorithms and systems to integrate the data and uncover large hidden values from datasets that are diverse, complex, and of a massive scale.

Big data is a collection of large amount of data which cannot be processed using traditional computing techniques.Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types.

• Structured data − Relational data.
• Semi Structured data − XML data..
• Unstructured data − Word, PDF, Text, Media Logs.

Adopting Big Data

As challenges faced by organizations to adopt Big Data technologies are discussed above, best practices to follow to address the challenges and adopt a proper Big Data technology can be done in 3 phases. Each phase address different challenges discussed above.

Having a cloud security well in business because the danger that is concerned with the firm is reduced. The security level is heightened by tracking exchanges and the applications of strong passwords. Thus most of the companies that have nee reporting are more concerned with the idea of cloud computing and adheres to the google encryptions for their servers

To start with the first phase, it is good practice to adopt the 3-legged Big Data environment that is integrated with each other. In this practice, Developers need to get an environment with access to a small volume of data and can be small scale cluster. Analysts need to have an environment with small scale cluster with enough volume and variety of data which can be used to prove the use cases. Business users need to have a large scale where they can use and test the application. All the three environments must be integrated so that developers, analysts, and business users can work collaboratively. Based on the small data and the business needs a use case id developed by the developers and is handed over to researchers. Researchers will test the various options of the use case with the help of different varieties of data they have access to and then hand it over to business users. Then business users will start using the use case in the research environment and either accept it or reject it. If the use case is rejected, then the researchers will fix it themselves or reach out to developers. This practice will help in addressing challenges like reusability, Scalability and maintainability and Manageability. The following diagram gives an insight into how the 3-legged Big Data environment works.

The second phase in the adoption of Big Data Technologies it that organization need to create an abstraction layer which is used for data modeling, integration, management, visualization to deal with a variety of data and helps to integrate other systems within the organization. This phase addresses the challenges like interoperability, security of the data and maturity about technology.

The third phase of adopting Big Data technology is to create a workbench around 3-legged environment and abstraction layer for a smooth transition of the use case from the development phase to until it gets deployed to the production environment. This phase mainly addresses challenges like Manageability, Scalability, and Maintainability. Having the same tools across all the environments will help to deploy back to the R&D environment for fixing the issues. This helps in taking care of issues that arise in management and monitoring.

Big Data plays a vital role in understanding valuable insights about customer requirements. Data from various sources which could be raw data, images, videos and could be data captured through credit cards, products leads to data growing. On analyzing data properly, data explains about their behavior and personalities. Companies can leverage these insights for improving product quality, business strategy, and marketing strategy to achieve customers requirements.