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.

With the continuous improvement of big data infrastructure, data analytics and business intelligence tools will gradually become the mainstay of big data. Therefore, the big data industry will develop toward these trends in the coming years.

In the digital age, the emergence of disruptive technologies has changed the nature of lending. Thanks to big data, the lending process is now less about the bank and more about the customer.

Big data has always been a relatively mysterious industry, in recent years because of big data discriminatory pricing only by more than the average person to understand, so have you ever thought about big data whether it is developed or analyzed, where the data inside are coming from?

With the advent of the digital age, data has become one of the most valuable assets in businesses and organizations. And data analytics is the key tool to turn this data into real value.

To ensure that your organization's big data plan is on track, you need to eliminate the following 10 common misconceptions. Let's look at them together.

In order to unlock the potential of advanced visualizations that enable organizations to analyze multiple sources of information and uncover hidden patterns and trends, certain challenges of leveraging big data should be addressed.