The data grid can overcome many challenges inherent in big data by driving higher levels of autonomy and data engineering alliances among a wider range of stakeholders. However, big data is not a panacea, it brings a series of risks for enterprises to manage.

With the increasing maturity of data analytics technology, research institutes should actively utilize data analytics tools to improve research efficiency.

Mastering big data programming skills is crucial for developers who want to succeed in the big data space.

Human beings and objects are the two major categories of the earth. Human beings are the most advanced animals on the earth. Objects (animals, plants, organisms, microorganisms, man-made objects) cannot be made. Human beings have wisdom and dominate the earth;

To do big data, first of all, you should understand what is the core of your own enterprise or industry. We often find that many enterprises are defeated not by their current competitors, but by many competitors who are not your competitors. For a simple example, everyone thinks that Amazon is an e-commerce company, but this is wrong. Its main revenue now comes from the cloud (cloud service). That is to say, enterprises need to find their own core data (value).

2013 is called the first year of big data, and all walks of life are gradually opening the era of big data applications. Until now, big data is still talked about.

Big data analysis is a complex process of analyzing a large amount of data to discover information such as hidden patterns, relevance, market trends and consumer preferences, which helps enterprises make better decisions.

When executives hear the term "big data", they naturally think of an amazing amount of available data. This data comes from e-commerce and omni channel marketing, or from connected devices on the Internet of Things, or from applications that generate more detailed information about trading activities.

The application of big data is just like the use of credit cards. The better you use it, the greater the income. On the contrary, can enterprises bear the cost of mistakes in big data? This article describes 6 major mistakes and solutions.

What we can predict is that the future of big data technology will continue to evolve along the direction of heterogeneous computing, cloudization, AI convergence, and in-memory computing.