What are the Core Technologies of Big Data Analysis?

Science and technology are the productive forces supporting development. Through the rapid development, big data has provided a variety of conveniences. But many people don't understand big data. They don't know any core technologies of big data analysis. This time, we will introduce the core technology of big data analysis.

From the perspective of the life cycle of big data, there are four aspects. There are big data collection, big data preprocessing, big data storage and big data analysis. Together, they become core technology of big data. Let's separate them as follows:

1. Big data acquisition. Big data collection is collection of structured and unstructured massive data from various sources. There are: database acquisition, network data acquisition, file collection.

2. Big data preprocessing. Before data analysis, a series of operations should be carried out on the original data. Data preprocessing is to improve quality of data and lay foundation for later analysis. Preprocessing includes four parts: data cleaning, data integration, data conversion and data specification.

3. Big data storage. This means to store the collected data in the form of a database. It includes three typical routes:
1. A new database cluster based on MPP architecture.
2. Technology extension and encapsulation based on Hadoop.
3. Big data all in one machine.

4. Big data analysis. From many aspects, the process of extracting, refining and analyzing the disorderly data. It includes visual analysis, data mining algorithm, predictive analysis, semantic engine and so on.

[1] Visual analysis
It refers to the analytical means to convey and communicate information clearly. And it can be effectively with the help of graphical means. It is mainly applied to massive data association analysis. Visual data analysis platform can help many things. And association analysis of distributed heterogeneous data is carried out. And make a complete analysis chart process. It has the characteristics of simple, clear, intuitive and easy to accept.

[2] Data mining algorithm
It creates a data mining model to explore and calculate data analysis means. It is the theoretical core of big data analysis.

[3] Predictive analysis
Predictive analysis is one of the most important application fields of big data analysis. It can combine a variety of advanced analysis functions. Like special statistical analysis, prediction modeling, data mining. As well as text analysis, entity analysis, optimization, real-time scoring, machine learning, etc. The purpose of predicting uncertain events is achieved.

[4] Semantic engine
It refers to the operation of adding semantics to existing data to improve users' experience.

[5] Data quality management
All kinds of data quality problems may arise in each stage of data lifecycle. Like planning, acquisition, storage, sharing, maintenance, application, extinction, etc. These problems need to be identified, measured, monitored, and other operations. Thus, it can improve the quality of data.

The above is to introduce the core technology of big data analysis. In fact, there are many specific frameworks for big data. And only these are the core parts.