What is the employment direction of big data?

, Big Data Development Engineer.

Big data development is mainly based on big data service platform, and many large and medium-sized business applications include enterprise-level applications and various types of business applications. Ability to build big data application platforms and develop analytical applications.

2. Big data analyst.

Big data analysts are mainly responsible for data mining, using technologies such as HIVE, HBase, etc., and are specialized in conducting industry research, evaluation, and evaluation for professionals engaged in industry data collection, collation, analysis, and data-based. By using Spotifre, QlikView, and Tableau, the new data visualization tools enable data visualization and data presentation.

There are two main directions:

The first is the direction of big data maintenance, R&D, and architecture engineers; The professional positions involved are: big data engineer, big data maintenance engineer, big data R&D engineer, big data architect, etc.;

the second is the direction of big data mining and analysis; The professional positions involved are: big data analyst, big data senior engineer, big data analyst expert, big data miner, big data algorithm, etc.

At present, there are three main directions for employment in big data: one is big data analysis talents, the other is system research and development big data talents, and the third is application development big data talents. Their basic positions are big data system R&D engineers, big data application development engineers, and big data analysts.

With the increasing number of types of enterprise data in the direction of big data employment, it has become very difficult to integrate and process data, and enterprises urgently need a person with data integration ability; DevelopmentWith the continuous increase of data scale, the data processing cost of traditional BI is too high, resulting in an increased burden on enterprises. The cheap data processing power of Hadoop has been rediscovered, and the demand of enterprises continues to grow. 3.Visual tool developmentThrough the operation interface elements, there are visual development tools to automatically generate relevant application software, and easily connect all data across multiple resources and levels; 4.

Information Architecture DevelopmentInformation architects must understand how to define and archive key elements to ensure that data is managed and utilized in the most efficient way; Key skills include: master data management, business knowledge, and data modeling;

Employment direction of big data:

The common positions I have come into contact with in big data include big data development posts, big data operation and maintenance posts, etc.

(1) Hadoop big data development.

The starting point of learning is high and difficult, and there are only a few training institutions on the market to do it.

Corresponding positions: data scientist, data mining engineer, machine learning engineer, etc. (2) data mining, data analysis > machine learning direction.

The market demand is strong, the main body of big data training, and the key corresponding positions of IT training institutions at present: big data development engineers, crawler engineers, data analysts, etc. (3) Big data operation and maintenance & cloud computing direction.

The market demand is medium, and it is more inclined to the corresponding positions of Linux and cloud computing disciplines: big data operation and maintenance engineers.

1. R&D engineer of big data system.

This professional is responsible for the research and development of big data systems, including large-scale unstructured data business model construction, big data storage, database construction, optimization of database architecture, solution of database center design, etc., at the same time, it is also responsible for the daily operation of data clusters and system monitoring, etc., this type of talent is a must for any organization that builds big data systems.

2. Big data application development engineer.

This type of talent is responsible for building big data application platforms and developing analytical applications, they must be familiar with tools or algorithms, programming, optimization, and deployment of different mapreduce, and they develop various applications and industry solutions based on big data technology. Among them, ETL developers are very sought-after talents, what they do is to extract data from different sources, convert and import data into the data warehouse to meet the needs of enterprises, extract the data in scattered and heterogeneous data sources such as relational data, flat data files, etc. to the temporary middle layer for cleaning, transformation, integration, and finally load into the data warehouse, which becomes the basis for online analysis and processing, data mining, and creates conditions for extracting various types of required data.

3. Big data analyst.

These people are primarily engaged in data mining, using algorithms to solve and analyze problems, to bring data to light, and to drive the continuous updating of data solutions. As the size of datasets continues to grow, the demand for Hadoop and related cheap data processing technologies such as HIVE, HBASE, MAPREDUCE, PIG, etc. will continue to grow.

Many industries have a big data mining engineer that may not be needed for a separate job. However, the industry is united, and big data technology needs to be practiced, and it may be better to be heroic. Good luck and hope to adopt.

In the field of big data, graduates with related majors have a very wide range of career options. From the Ministry of National Defense, Internet start-ups to financial institutions, from retail finance to Internet e-commerce, from medical manufacturing to traffic inspection, big data projects are needed to be driven by innovation, the demand for big data is everywhere, and their job remuneration is also very lucrative. In Silicon Valley, entry-level data scientists already earn six-figure (dollars); In China, taking Hadoop development engineers as an example, the entry salary of Hadoop has reached more than 8K, and the annual salary of Hadoop talents with 2-3 years of work experience can reach 300,000-500,000.

1. Big data system R&D engineering traveler.

2. A large number of Bu Fan according to the application development engineer.

3. Big data analyst.

4. Data visualization file fraud engineer.

5. Data security R&D talents.

6. Data science research talents.

The specific division is withered. It is not fixed, and the root group hole is adjusted according to the characteristics of each **. In 30 seconds, the picture is completely new. At this time, do you think that the "stake" on the right side of the picture is more abrupt and not good-looking.

Big data system R&D engineer, big data application development engineer, etc., there are still many employment directions.

There are many employment directions for big data, and you can be a data engineer or a development engineer.

After graduating from big data, I went to work in ETL R&D, Hadoop development, and information architecture development.

1. ETL is responsible for extracting the data in the distributed and heterogeneous data sources, such as relational data and flat data files, into the temporary middle layer for cleaning, transformation, integration, and finally loading into the data warehouse or data mart, which becomes the basis for online analysis and processing and data mining.

2. Hadoop is a distributed system infrastructure developed by Apache Club. It allows users to develop distributed programs without understanding the underlying details of distribution, and make full use of the power of clusters for high-speed computing and storage. The core design of the Hadoop framework is HDFS and MapReduce, which provides storage for massive amounts of data, and MapReduce provides computing for massive amounts of data.

1. Hadoop development engineer.

Hadoop is a distributed file system, referred to as a software framework that can process large amounts of data in a reliable, efficient, and scalable way.

2. Data analyst.

Data analyst is a kind of data engineer, which refers to professionals in different industries who specialize in industry data collection, collation, analysis, and make industry research, evaluation and evaluation based on data. In the work, through the use of tools, extract, analyze, and present data, to achieve the business significance of data.

3. Data Mining Engineer.

To do data mining, we need to find rules from massive data, which requires certain mathematical knowledge, such as linear algebra, advanced algebra, convex optimization, probability theory, etc.

1. Data storage and management.

Big data starts with data storage. This means starting with Hadoop, a big data framework. It is an open-source software framework developed by the Apache Foundation for the distributed storage of very large data sets on computer clusters.

Clearly, storage is essential for the vast amount of information required for big data. But more importantly, there needs to be a way to centralize all of this data into some sort of formative management structure that generates insights. Therefore, big data storage and management is the real foundation, and it will not work without such an analytics platform.

In some cases, these solutions include employee training.

Big data employment direction.

2. Data cleaning.

Before businesses can actually process large amounts of data to gain insights, they need to clean, transform, and turn it into something that can be retrieved remotely. Big data tends to be unstructured and unorganized, so it needs some sort of cleansing or transformation.

In this day and age, data cleansing becomes even more necessary because data can come from anywhere: mobile networks, IoT, social**. Not all of this data can be easily "cleaned" to generate its insights, so a good data cleansing tool can make all the difference.

In fact, in the coming years, effectively cleaned data is seen as a competitive advantage between an acceptable big data system and a truly great data system.

Big data employment direction.

3. Data mining.

Data mining is in many ways the true core of the big data process. Data mining solutions are often very complex, but striving to provide an interesting and user-friendly user interface is easier said than done. Another challenge for data mining tools is:

They do require staff to develop queries, so data mining tools are no more capable than professionals using them.

Big data is a term that describes large amounts of data, both structured and unstructured, that cover a large number of businesses on a daily basis. But it's not the amount of data that matters, it's the way important data is processed, and big data can be analyzed for better decision-making and strategic business changes1, Hadoop development engineer2, data analyst3, data mining engineer.

If you are passionate or interested, you will be relatively easy to accept the relevant professional knowledge, whether it is studying or working, you will not feel very boring, the working environment is good, the employment prospects are good, the salary is high, it is a good major.

1. Hadoop Development Engineer 2, Data Analyst 3, Data Mining Engineer.