What Does a Big Data Engineer Do? What does a big data engineer do?

The main work of a big data engineer is: analyzing the past, improving the future, and optimizing choices.

1. Analyze history and find out the characteristics of past events

An important part of the big data engineer's job is to analyze the data to find out the characteristics of past events. Identifying the characteristics of past events can be most useful in helping businesses better understand consumers. By analyzing the user's past behavior trajectory, we can understand the person and his behavior.

2. In the future, what may happen in the future:

By introducing key factors, big data engineers can ** future consumer trends.

3. Optimize the selection and find out the most optimized results

Depending on the nature of the business of different enterprises, big data engineers can achieve different purposes through data analysis.

In their jobs, big data engineers need to build data analysis platforms based on Hadoop and Spark to design and develop distributed computing services. Responsible for the construction, performance tuning, and daily maintenance of the cluster environment of big data platforms (such as Hadoop, HBase, Spark, etc.). Responsible for data warehouse design, data ETL design, development and performance optimization.

Participate in the construction of a big data platform and build user portraits based on big data technology.

In simple terms, a big data engineer is responsible for creating and maintaining a data analytics infrastructure, including the development, building, maintenance, and testing of big data architectures, such as databases and big data processing systems. In addition, the Big Data Engineer is also responsible for creating processes for modeling, mining, acquiring and validating data collections.

Shen Zhiyong believes that if big data is imagined as a constantly accumulating mine, then the job of a big data engineer is, "The first step is to locate and extract the data set where the information is located, which is equivalent to prospecting and mining." The second step is to turn it into information that can be directly judged, which is equivalent to smelting. Finally, there are applications, visualization of data, etc.

ThereforeAnalyze the history, the future, and optimize the selectionThese are the three most important tasks for big data engineers when "playing with data". Through these three directions of work, they help businesses make better business decisions.

Generally speaking, big data is a position responsible for the company's Internet data analysis, simply put, such as shopping ** on the recommendation of some products to you according to your browsing, in addition to analyzing the information that has a guiding role in the company's discovery from massive data, etc., are all big data needs to do, and now the demand for big data is very large, and it has great development prospects.

In order to thoroughly implement the spirit of the "Action Plan for Promoting the Development of Big Data" (Guo Fa (2015) No. 50), actively respond to the Ministry of Industry and Information Technology's "Big Data Industry Development Plan (2016-2020)", promote the construction of big data infrastructure with advanced technology and prosperous applications, and further improve the theoretical and technical level of professional and technical personnel in big data engineering.

Big Data Engineer, Big Data Maintenance Engineer, Data Miner, Big Data Algorithmist.

Big data development direction: The positions involved include big data engineers, big data maintenance engineers, big data R&D engineers, big data architects, etc.

Data Mining, Data Analysis and Machine Learning: Positions such as Big Data Analyst, Big Data Senior Engineer, Big Data Analyst Expert, Big Data Miner, Big Data Algorithm, etc.

1. Improve program design capabilities. Hands-on practical ability has a very direct impact on the employment of undergraduates, especially in the early stage of the current application of big data, many application-level positions have not been released, and many technical teams pay more attention to students' programming ability, so it is more important to have a solid programming foundation.

2. Master certain knowledge of cloud computing. Big data itself is very closely related to cloud computing, and it is necessary to master a certain amount of cloud computing knowledge in the future, whether it is engaged in big data development or big data analysis positions. Mastering cloud computing knowledge will not only improve your work efficiency, but also expand your technical boundaries.