High salary and high quality
More opportunities
,
Gap is big
**, so that big data in the development circle became xiangbobo.
At the same time, during my two years as an official account, I have witnessed too many people “from entry to give up”, and some people have not even entered the door of big data. Which one are you?
I have done big data in small and medium-sized enterprises for a period of time, but I only do a small piece of work in the whole process of big data, and I have no idea about the whole process and how to select the type. Moreover, the data level of the company is not enough, so it is difficult to move to a big factory.
I studied big data by myself for a while, but I only learned superficial things. There was no database to simulate storage and calculation, and I only dared to write “understand” certain technology on my resume. Finally, I couldn’t even find a job.
If you want to switch to big data, it’s hard to get into it
.
Above these several cases, a look is **
Never worked on a real project
Also,
No systematic training
支那
As a result, the salary of doing big data is high, but the threshold is also high, because no matter what level you are, you should have used the required technology stack, otherwise, let alone big factories, it is difficult to enter small and medium-sized enterprises.
So how do you learn big data? Today, I would like to talk with you about my learning path and method.
**** Stage 1, Master Java Web data visualization
. You need to master
Java server-side technology, front-end visualization technology, database technology, this stage is mainly to reserve the pre-big data skills, of course, you can work as a data visualization engineer, but it is not really a big data entry.
****2, learn the Hadoop core and ecosphere technology stack
. This section covers a lot of technology, like
HDFS distributed storage, MapReduce, Zookeeper, Kafka and so on should be mastered. After mastering them, I can work in some big data positions such as ETL engineer, but my knowledge reserve is not complete enough.
**** stage 3, solve the calculation engine and analysis algorithm
. Computing engines I suggest
Both Spark and Flink can be used proficiently, and while Spark is still used by some enterprises, Flink will definitely become mainstream in the future. With this knowledge, you will have a relatively complete set of big data skills that will enable you to take on some high-paying jobs, such as big data r&d engineer, recommendation system engineer, user portrait engineer, etc.
I collect some internal data of the big data industry, including **
Big Data Engineer Manual,
Big data development learning roadmap, as well as Meituan, byte and other large manufacturers
Interview questions,
** want students can scan the code for free.
delta
Scan code to obtain information