Big data has broad development prospects, more and more application fields, and there are many types of big data technologies, which makes many people who learn big data and related technologies intimidated and do not know where to learn big data. Especially for some novices, how to quickly learn big data has become a difficult problem to solve. This article will provide Xiaobai with an effective learning path, hoping to help Xiaobai learn big data better.
Before learning about Big Data, we should be clear that most of the technical applications of Big Data are written in Java or Scala. Of course, from the mainstream of the market, Xiaobai is recommended to learn java. After all, the language for writing the entire big data application is javase. But one thing Xiaobai needs to pay attention to is that we are learning big data instead of learning java development. We need to distinguish between primary and secondary. When learning java, we only need to learn reflection in javase content, jdbc application, etc. It is enough, if you are willing to learn other You can study by yourself. The distinction between primary and secondary can allow Xiaobai to learn big data better and faster, instead of wasting time in places that are not needed.
Nowadays, there are a lot of comments on the Internet about learning big data and only learning hadoop, but for those who are engaged in the big data technology industry, is it really okay to only learn hadoop? The answer is of course no. We all know that hadoop is an open source software mainly used for distributed storage and computing. It is composed of HDFS and MapReduce computing frameworks, which are the open source implementations of Google's GFS and MapReduce respectively. Due to its ease of use and scalability, hadoop has become a popular massive data processing framework recently. Although hadoop is the focus of learning big data, there is one factor that everyone must consider, such as hadoop, hbase, spark, etc. all run on linux, so learning big data must learn linux.
For javaee, it can actually be ignored, because there is a platform for data display. It needs to be built on the server. When it is opened, it is a data display system. This system is made by people who learn javaee, and those who do big data technology store data. In the table, let the background staff make a display, the data results are made by us, and the display platform is made by the background developer javaee, so the necessity for us to learn javaee is not so strong. For Xiaobai, we can first These neglects, when you have time or energy, it is not too late to study.
Perhaps after analyzing these, Xiaobai still feels confused, so the following big data learning outline may give you a clearer understanding of how to learn big data.
First, the learning of java. Java is the foundation, so we must first learn, the main aspect is javase, of course, javaee can also learn, but it can be weakened;
Second, the learning of linux and hadoop. Because hadoop runs on linux, the two can be learned together. Of course, it is best to learn linux first and then learn hadoop;
Three, hive and oozie learning. Hive is a data warehouse infrastructure built on Hadoop, so the learning of hive should be placed after hadoop. Hive provides a series of tools that can be used for data extraction, transformation and loading (ETL), which is a mechanism for storing, querying and analyzing large-scale data stored in Hadoop, and a skill that big data talents must master. . For oozie, it is a workflow scheduling engine for the Hadoop platform, so the relationship among hive, oozie, and hadoop is integrated, and hive and oozie are studied together after hadoop, not only in line with the correlation of knowledge, but also more It is easy for Xiaobai to understand;
Fourth, the learning of web and flume, the content of these two parts is not much, but it does not mean that it is not important, especially Xiaobai, the content of these two parts should be studied in detail;
Five, pathon and hbase learning, pathon is a programming language, its importance is self-evident. HBase is a distributed, column-oriented open source database, different from general relational databases, it is a database suitable for unstructured data storage, and this is also the focus of learning big data;
Six, kafka and scala learning. The purpose of Kafka is to unify online and offline message processing through Hadoop's parallel loading mechanism, and to provide real-time messages through clusters, which is very important for big data. For scala, it is another language, which plays a fundamental role in the learning of apark.
Seven, the learning of spark and spark tuning, the reason why the two are studied separately is because their knowledge points are many and relatively complex, and another reason is that spark is also one of the new development directions of big data in the future, so Xiaobai is more should learn.
Eight, project training, this is a very important link! The technology you learn will be converted into implementation in the project training session, and the technology you master will eventually be used for project development.
9. Continue to improve your ability and technology at work!
Xiaobai's "path" to learn big data is actually not as complicated as he imagined. With a systematic study plan and a reasonable study time arrangement, Xiaobai's efficiency in learning big data will be very high. The era of the future will not be the era of IT (Internet technology), but the era of DT (data technology). Big data will flourish in the next ten years, and will also help enterprises improve production efficiency and help realize smart cities and smart medical care. Of course, more deeply into our daily life. Today, big data is developing in coordination with technologies such as artificial intelligence, mobile Internet, cloud computing, and the Internet of Things, and the number of talents required by the market will increase. Therefore, entering the field of big data is not only the best choice for Xiaobai, but also The best choice for some ideal, aspiring people.
This article comes from the big data training of Manatee Academy