Regarding the data processed, a big
Posted: Wed Jan 08, 2025 3:18 am
Broadly speaking, Big Data and IT projects may seem similar. However, they differ in their purpose and in the use made of the data they manipulate.
Already, their objectives diverge. IT projects aim to create and implement applications and software, or even complex IT structures, and all the processes that result from them, such as systems management. For example, developing websites or smartphone applications, or even social networks (development cambodia phone data remains on the IT side while management will be more Data-oriented). Big Data projects aim to collect data, store it, process it, on significant volumes to extract trends, which are called insights. This data is then used to improve the system (again on the IT side), or even create predictive or decision-making models. Data projects therefore seek to analyze user behavior , detect trends, potential risks, and are used for the ultimate optimization of software.
difference appears when we look at the volumes, speeds and varieties of these. For example, IT projects process moderate volumes and only use the data necessary for their system improvement and management activity. The processing speed is relatively low and remains on a human scale, which limits the investments required in powerful servers and computers. Very often, the analysis is done by humans. On the Data projects side, on the other hand, the quantity of data processed often reaches Terabytes , or even Petabytes . Hence the need for very high throughput and advanced automation to structure the databases. These concern all the data that can be collected, down to the time it takes a cursor to hover over a logo. The analysis of such a quantity of data is of course devolved to very high capacity computers and servers, which are very expensive. The solution may lie in decentralized design, like blockchains , which allows all users to be part of the same network and therefore share part of their resources for the benefit of the proper functioning of software and the IT structure.
Already, their objectives diverge. IT projects aim to create and implement applications and software, or even complex IT structures, and all the processes that result from them, such as systems management. For example, developing websites or smartphone applications, or even social networks (development cambodia phone data remains on the IT side while management will be more Data-oriented). Big Data projects aim to collect data, store it, process it, on significant volumes to extract trends, which are called insights. This data is then used to improve the system (again on the IT side), or even create predictive or decision-making models. Data projects therefore seek to analyze user behavior , detect trends, potential risks, and are used for the ultimate optimization of software.
difference appears when we look at the volumes, speeds and varieties of these. For example, IT projects process moderate volumes and only use the data necessary for their system improvement and management activity. The processing speed is relatively low and remains on a human scale, which limits the investments required in powerful servers and computers. Very often, the analysis is done by humans. On the Data projects side, on the other hand, the quantity of data processed often reaches Terabytes , or even Petabytes . Hence the need for very high throughput and advanced automation to structure the databases. These concern all the data that can be collected, down to the time it takes a cursor to hover over a logo. The analysis of such a quantity of data is of course devolved to very high capacity computers and servers, which are very expensive. The solution may lie in decentralized design, like blockchains , which allows all users to be part of the same network and therefore share part of their resources for the benefit of the proper functioning of software and the IT structure.