We live in a world increasingly driven by data. How your organization defines its data strategy and approach — including its choice of big data and cloud technologies — will make a critical difference in your ability to compete in the future.
Big Data for business process improvement
The term "big data" is subject to continuous change. It is also often described as a complex of technologies used to collect and evaluate these data sets. The collected data can come from various sources.
Big Data can generate business process improvements in all functional areas of companies, e.g. in the area of technology development and information technology as well as marketing.
Large companies and sectors of the economy, market research, sales and services management, medicine, administration and news services have used the corresponding digital methods: the data collected is to be further developed and used in a profitable way. Today, data collection is also used by small and medium-sized enterprises to carry out trend research in the social media and advertising analyzes in order to identify and react to future-oriented and possibly profitable developments.
Processing Big Data
Unstructured and semi-structured data types typically do not fit well into traditional data warehouses based on relational databases that are oriented to structured data sets. In addition, Data Warehouses are unable to meet the processing requirements imposed by large data sets that need to be updated frequently - or even continuously, such as real-time data on stock trading, the online activities of website visitors, or the performance of mobile applications.
Organizations that collect large data, process and analyze frameworks that allow the processing of large data sets across clusters of computers with simple programming models. These solutions have been designed, among others, to be used as landing place and staging areas for data before being loaded into a data warehouse or an analytical database for analysis, usually in a combined form, which is conducive to relational structures.
A data lake, which serves as the primary repository for incoming data streams, is more frequently used. In such architectures, data can be analyzed directly in a cluster or run through a processing machine. As with the data warehouse, sound data management is an important first step in the large data analysis process. Data stored in a Big Data solution must be properly organized, configured, and partitioned to provide good performance for both extract, transform, and load (ETL) integration jobs, as well as analytical queries.