Comparing different approaches for retrieving and visualizing big data

Abstract

Big data is a huge part of companies’ success nowadays and therefore the big data needs to be retrieved and analysed to benefit from it. Big data has taken a remarkable role in business and therefore it is important that companies put effort into their big data analyses. With the data analyses companies can follow data trends in different areas, and notice increasing or decreasing trends quickly. These allow the company to act prior and predict what will be happening in the future. Therefore, companies can act on upcoming changes beforehand.

For the data retrieval and analysis, there are multiple ways to execute that. In this research, there are two methods that are considered for data retrieval for the purpose of data analysis. This research will build missing management reports to bring even more benefit from the big data. The two methods will be covered in this research and consider their possibilities and limitations. Elasticsearch, APIs and Python are at the key position when investigating and building data retrieval for the management reports. As Elasticsearch, APIs and Python are for the data retrieval the data also needs to be visualized to create understandable management reports. The visualization will be done in Power BI to visualize the data in graphs and to create useful filters for the users to limit the graphs as they want them to. This research is done following the Design Science Research (DSR) research method.