As programmers, we all have the same anxiety — that is, when the wave of new technology comes one after another, we can’t help but desperately follow it, always worried that if we don’t keep up with the trend of new technology, we will be abandoned by The Times.

In particular, in recent years, technology wave after wave, from the Internet of Things, cloud computing, big data, VR/AR, artificial intelligence, automatic driving, and now the blockchain, each wave of technology has been extremely hot by capital and market. And a large number of programmers have joined the startup army of hot technology, which undoubtedly increases the anxiety of friends around.

In fact, similar to such anxiety is very normal, people will have immediate worries, but also human nature. There is an ancient saying: “In times of peace, be prepared, be prepared, dare to rule.”

But after all, people’s energy is limited, it is impossible to follow every wave of technological upsurge, how to choose in the end, I think we are also full of questions.

In the long run, it must be the best technology that is slow to be eliminated, proportional to the accumulation of experience, easy to form knowledge barriers and not easy to be replaced. But in practice, it’s hard to find that kind of technical field, which is why programmers generally feel insecure. Take Java language development for example, what’s the difference between working for five years and working for three?

But in fact, there is one area of technology that has such potential: big data. Gao Yang, senior big data architect and big data expert of Kingsoft, once said that for project management and higher-level employees, big data can help them to think more and look at the logic of data dialectically, and understand what technologies can do and what advantages they have in popular learning and work. Such knowledge is useful for managers to make judgments about the current state of technology, to estimate the difficulty and cost, and to broaden the imagination in innovation.

Meanwhile, according to relevant survey data, the employment salary of big data talents is generally higher. Take Beijing as an example, the average monthly salary of big data developers is 30230, data analysts 11130, Hadoop engineers 20130, data mining 21740, algorithm engineers 22640, isn’t it very attractive?

The average annual salary of big data engineers, AI engineers and all engineers is 292,200 yuan, 299,800 yuan and 237,300 yuan, respectively, for those with less than three years of service. Among those who have worked for eight to 10 years, their average annual salaries are 442,300 yuan, 457,100 yuan and 399,100 yuan, respectively. It can be seen that in the field of big data, salary increases greatly with the increase of working years.

The reason why big data is expected is that data has gradually become the core competitiveness of enterprises. By analyzing and mining the value of data, enterprises can know customers’ needs in advance and predict their consumption habits and trends. Let all decisions of managers have a basis to rely on, no longer blind, reduce enterprise risks.

In the past two years, the wave of digital transformation has swept all walks of life, and more and more traditional industries have begun to realize the value of data. Suhabi Abbas, former chairman and CEO of Informatica, once said that the single most valuable asset of the information age is data, which is needed to better understand customers and improve the efficiency and agility of businesses.

According to third-party forecasts, by 2020, every Internet user will generate 1.5GB of data per day, a smart factory will generate 1PB of data per day, and cloud video service providers will generate up to 750PB of video data per day.

It can be seen that the scale of data will reach an unprecedented order of magnitude in the future, and enterprises’ demand for data management will also be greatly improved, especially for big data talents.

Last year (2017), a developer survey was conducted, and the survey results showed that the main problem faced by enterprises in building big data platforms is the lack of talent. Of course, big data application planning and technology selection is also a realistic problem plaguing enterprises.

Big data involves many technologies and many common frameworks, such as Hadoop, Spark, Storm, SciKit-Learn, Mahout, TensorFlow, etc. Where should you learn to help your career?

In this regard, the author really dare not say nonsense, after all, professional things must be handed over to professional technical bull to answer more secure.

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