How to Understand Big Data

After years of development, big data has more conceptual meanings. From a different perspective, big data will have different definitions, but in general, big data can be summarized in three aspects The first is "new value field"; the second is "data value"; the third is "the foundation of industrial Internet".

One of the important reasons why big data has received widespread attention is that big data has opened up new value areas, which is very critical. The new value field will create a series of ecosystems, and the ecosystem will breed a large number of different business models, and this process will also be accompanied by a lot of innovation, so there are currently many entrepreneurial projects based on big data. Big data has provided value to the endless data on the Internet (Internet of Things), and the whole society has a new understanding of the Internet.

Big data technology mainly revolves around the core of "data value" and involves technologies such as data collection, data collation, data storage, data security, data analysis, data presentation, and data application. Data collection technology involves the Internet of Things technology. In fact, the Internet of Things is also the main data source of big data, so the relationship between big data and the Internet of Things is also very close. It can also be said that there will be no big data without the development of the Internet of Things.

The most common data sorting technology is data cleaning. Conventional data cleaning technology involves the use of regular expressions and SQL language, as well as the rationality, authenticity and integrity of data according to specific business rules. Data storage technology mainly involves database technology, including both Sql database and NoSql database.

Data analysis technology is one of the core links of the big data technology system. Data analysis needs to adopt different analysis technologies according to different data types. At present, the main data analysis methods include statistical analysis methods and machine learning methods. Mastering certain algorithm knowledge and programming knowledge is the premise of data analysis. For beginners, you can start from the Python language and learn how to use Python language to complete algorithm implementation and training.