Learn from the big data industry? Data storage and management? Big data starts with data storage. This means starting with Hadoop, the big data framework. Apache wrote it? The open source software framework developed by Foundation is used to distribute and store very large data sets on computer clusters. ? Obviously, it is very important to store a lot of information needed for big data. But more importantly, there needs to be a way to concentrate all this data into some kind of information/management structure to generate insight. Therefore, the storage and management of big data is the real foundation. Without such an analysis platform, it will not work. In some cases, these solutions include employee training. ? Second, data cleaning? Before an enterprise can really process a large amount of data to gain insight information, it needs to clean up, transform the data and turn it into content that can be retrieved remotely. Big data is often unstructured and unorganized, so it needs to be cleaned up or transformed. ? In this era, data cleaning becomes more necessary, because data can come from anywhere: mobile network, Internet of Things, social media. Not all these data are easy to be "cleaned up" to produce opinions, so a good data cleaning tool can change all the differences. In fact, in the next few years, effectively cleaned data will be regarded as an acceptable competitive advantage between big data systems and truly excellent data systems. ? Three data mining? Once the data is cleaned up and ready for inspection, you can start the search process through data mining. This is the process of enterprise's actual discovery, decision-making and prediction. ? In many ways, data mining is the real core of big data processing. Data mining solutions are usually very complex, but providing an interesting and user-friendly user interface is easier said than done. Another challenge faced by data mining tools is that they do require staff to develop queries, so the ability of data mining tools is not better than that of professionals who use them. ? Four data visualization? Data visualization is a way to display enterprise data in a readable format. This is how companies view charts and graphs and see through data. ? Like science, data visualization is an art form. There will be more and more data scientists and senior managers in big data companies, so it is very important to provide employees with a wider range of visualization services. Everyone in the sales representative, IT support, middle management and other teams needs to know about it, so the focus is on usability. However, easy-to-read visualization is sometimes inconsistent with the reading of depth feature sets, which has become a major challenge for data visualization tools. ?
Understand the employment prospects of big data? Because the value created by big data is very large, it will also make enterprises more willing to pay higher salaries for relevant talents. At present, the monthly salary of practitioners with one year's work experience has reached about 15k. The annual salary of practitioners with 3-5 years experience has reached about 300,000-500,000. The employment prospect of big data is worth looking forward to, and it is also necessary to enter big data early. ? There are many employment directions of big data, which can be mainly divided into three categories:? 1. Big data development direction: big data engineer, big data development engineer, big data maintenance engineer, big data R&D engineer, big data architect, etc. 2. Data mining, data analysis and machine learning direction: big data analyst, big data senior engineer, big data analyst, big data miner, big data calculator, etc. 3. Big data operation and cloud computing direction: big data operation and maintenance engineers, etc? It's the job-hunting season. The big data industry is a high-paying industry. The following jobs and relative salaries can be used as a reference for those who are willing to engage in the big data industry. ? 1，ETL R&D
ETL, the abbreviation of English Extract-Transform-Load, is used to describe the process of extracting, transforming and loading data from source to destination. The word ETL is often used in data warehouse, but its object is not limited to data warehouse. ? Skills required: ETL engineers are professional technicians engaged in system programming, database programming and design, and must master various commonly used programming languages. Therefore, engaged in ETL research and development must first have excellent programming skills, and secondly be familiar with mainstream database technologies, such as oracle, Sql? Server, PostgeSQL, etc. And understand data etl development tools, such as Datastage, Congos, Kettle, etc. ? 2.Hadoop development
The core of Hadoop is HDFS and MapReduce. HDFS provides mass data storage and MapReduce provides data calculation. Hadoop developers use Hadoop to process data when necessary. ? Skills required: one or more of Java/Scala/Python/C/C++/JavaScript/JSP;