Recently, there is an article on the Internet about the current situation of Java programmers' workplace survival "Java programmers in 2017, the crisis behind the scene", which has aroused widespread attention and heated discussion in the Java programmer circle.
In 2017, Java programmers faced even tougher competition.
I have to admit that after the rapid development of the industry, the overall development of the Internet has stabilized. Why do you say that? Why put it under the inventory of Java programmers?
Indeed, for Java programmers who can attack the front-end and defend the back-end base camp, although the supply is rising year by year, the market still seems to favor them. Can these Java programmers with high supply and high demand in the technical recruitment market really still be so beautiful in the 17-year recruitment market? Or lay some foreshadowing of "crisis"?
After research by 100offer, it was found that Java programmers in 2017 were indeed very good, which was reflected in the increase in market demand and average salary, but after these good times, some crises ambush:
1) The growth in market demand is far less than the growth in the supply of Java programmers, and Java programmers face more intense competition;
2) Affected by the substantial increase in supply, the average salary increase of Java programmers is relatively low;
3) The demand side puts forward more stringent selection criteria for Java programmers.
The increased competition for Java programmers looking for jobs in the recruiting market in 2017 is mainly due to two reasons:
1) The supply of Java programmers increased by 25% compared to 2016;
2) The increase in demand for Java programmers is much lower than the increase in supply.
In the context of growing supply, Java programmers also saw lower salary increases.
The platform network believes that the fierce competition faced by Java programmers in 2017 will continue in 2018 and will intensify.
The most fundamental reason is that Java, as a mainstream programming development language and workplace skills, has been recognized by more and more fresh graduates and newcomers in the workplace. More and more people are learning Java programming technology through various training institutions or online courses, which is continuously increasing the supply of Java programmers.
In the eyes of fresh college students, rookies and non-IT people, learning Java technology to become a Java programmer is undoubtedly a good choice, which at least to some extent solves their top priority-survival problem. However, for Java programmers who have been in the industry for 2-3 years, the survival problem has been alleviated and solved, and they are more concerned about, "Can this road continue?", "Can the salary continue to increase next year? more than 30%”.
For programmers, technology is always evolving, and keeping up with the times is a must. There is a huge shortage of big data talents, and the opportunity for transformation has appeared. If you can seize this rare opportunity and transform into a big data engineer, so that you can go further (competitiveness, money, etc.), why not do it?
Big data is engaged in open source work, and it is more inclined to "R&D", which can reignite the enthusiasm of programmers to develop programs, and have new pursuits in their careers, which means that big data will become a work worthy of programmers' long-term struggle and continuous breakthroughs. Second, because big data is an emerging field, there is a shortage of professional talents, and high-end talents are the objects of competition for enterprises. Salary is easy to rise and career development potential is huge.
It is also good to do Java, but big data is a trend at present. Companies with a little more strength are working on big data projects, and Hadoop itself is developed in Java. In addition, Hadoop engineers are generally more than 3k higher than pure Java development. Therefore, many Java practitioners are turning to hadoop big data.
There are already more people doing Java. Many people work for 4 to 5 years and their monthly salary is difficult to reach 20,000, and even fewer can earn 25,000. But many people in Hadoop get more than 20,000 with only 1 year of experience. Therefore, many people who are well-paid now also come to the podium to learn Hadoop, mainly considering the issue of future development ceilings.
If you are in a management position in Java for 5-6 years, the salary can basically reach 20,000-25,000. But 25,000 is basically the ceiling of Java technicians, and there are very few people who can reach this number, unless they are architects or doing low-level development. However, the salary of more than 20,000 yuan in Hadoop can only be regarded as average, and there is still a lot of room for development, so many experienced Java veterans are turning to this area.
Old age is a big problem for those who are engaged in technology. Java engineers are all over the street. When you get older, your salary is good, but your energy can’t keep up with young people. You can’t work overtime. . The age of Hadoop has little impact, because it is not simple programming to engage in big data, and the weight of programming is not even 1/6. In many cases, you need to analyze and solve problems from multiple aspects such as server, storage, computing, and operation and maintenance. , The older you are, the more experienced you are, and the more popular you are.
What skills does a big data engineer need?
(1) A background in mathematics and statistics;
(2) Computer coding ability;
(3) Knowledge of a specific application area or industry.
An important point of the role of a big data engineer is that it cannot be separated from the market, because big data can only generate value when combined with applications in specific fields.
Therefore, experience in one or more vertical industries can accumulate industry knowledge for candidates, which is very helpful for becoming a big data engineer later, so this is also a more convincing bonus when applying for this position.
There are many skills related to big data. According to the data itself, it can be divided into five categories: data acquisition, data processing, data analysis, data storage, and data mining.
Data acquisition: log collection Scribe, Flume and crawlers, etc.;
Data processing: stream computing storm, spark streaming, Hadoop, message queue related such as Kafka, etc.;
Data analysis: HIVE, SPARK, basic algorithms, data structures, etc.;
Data storage: HDFS, etc.;
Data mining: machine learning related algorithms, clustering, time series, recommendation systems, regression analysis, text mining, Bayesian classification, neural networks, etc.
Finally, the teacher from the podium gave 3 suggestions to engineers who are transforming into big data.
(1) Pay attention to the foundation;
(2) Exercising expertise;
(3) To like & to persist.
Friends who are interested in big data engineers, the teacher of the big platform network will send you two words: how many times can you fight in life, and when you don’t fight at this time. Regardless of success or failure, there are many personal experiences and insights. Friends who are looking for big data learning resources can also go to the big platform network .