These data sets are often so large and complex that normal data processing software can hardly collect, store and process them in a reasonable amount of time, because their volumes can increase. to petabytes or even Exabytes. Big data is made up of sources including websites, social media, desktop and mobile apps, scientific experiments, sensors and other devices on the internet.
Once data is available, the most important thing is to analyze that data so it can actually benefit from increased revenue, improved customer service, improved performance, and increased overall competitiveness.
Data analysis involves examining data sets, from which details or conclusions can be drawn about trends and forecasts about future activity. As a result, data can be applied to jobs such as choosing time and place to advertise products and services, which is very useful for companies that need product-based marketing campaigns from time to time.
In order to effectively use and exploit Big Data, it is necessary to build an infrastructure sufficient to collect and store data, provide access and security of information during storage and transfer. Next, include storage and server systems, management software, data integration, data analysis software and a number of other components. This can be realized by large data centers, cloud services also contribute to solving this problem.
Data can come from a variety of sources such as web applications, social media channels, mobile apps and email storage, in addition, when the IoT becomes explosive, the sensors in the product. also contribute to data creation and transformation. To store all incoming data, there are a number of common options that are traditional data warehouses, building data pools or taking advantage of cloud storage.
In addition, it is necessary to build security infrastructure, including data encryption, user authentication and access rights, system monitoring, firewall construction, enterprise management and products. other to protect the system and data.