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 urgent need for data for enterprises that are reshaping their platforms for the digital age has spurred a proliferation of IoT implementations across multiple industries, including industrial manufacturing, agriculture and retail.

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

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.

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.

Although big data may seem advanced, but in these years of development, there have been many cases close to our lives, but we may not realize that this is actually "big data" in action.

The British science and technology news media V3 recently listed 10 relevant misconceptions about big data applications.

Data silos and unlinked systems caused employees to waste a lot of time moving information around. In addition, the sheer volume of paper and electronic forms forced employees to manually process documents and verify their contents.