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.

Companies tend to make their Big Data projects large in size and scope when implementing them, but the truth is that most Big Data projects usually end up in failure.

Big data has been closely related to our life, many enterprises have started to use big data, even a small thing in our life may be related to big data.

The industry discussion on cloud migration has focused on how to redesign applications to take full advantage of cloud services and then migrate them to the cloud.

With the gradual development of big data, there is more and more data, and data analysis is especially important.

Cloud technology is a way of sharing computing resources connected through the Internet. These resources include computers, storage devices, network devices, and applications.

Python as a recognized language suitable for big data, want to do big data development and big data analysis, not only to use Java, Python is also very important a core.