Shulou(Shulou.com)06/02 Report--

This article is to share with you what big data has learned. The editor thought it was very practical, so I shared it with you as a reference. Let's follow the editor and have a look.

1. Java programming

Java programming is the basis of big data's development. Many technologies in big data are written in Java, such as Hadoop, Spark, mapreduce and so on. Therefore, if you want to learn big data well, Java programming is a necessary skill!

2. Linux operation and maintenance

The development of enterprise big data is often completed under the Linux operating system, therefore, if you want to work related to big data, you need to master the operating methods and related commands of the Linux system.

3 、 Hadoop

Hadoop is a software framework capable of distributed processing of a large amount of data, HDFS and MapReduce are its core design, HDFS provides storage for massive data, and MapReduce provides computing for massive data, which is an indispensable framework skill for big data's development.

4 、 Zookeeper

ZooKeeper is a distributed, open source distributed application coordination service, an open source implementation of Google's Chubby, and an important component of Hadoop and Hbase. It is a software that provides consistency services for distributed applications, including configuration maintenance, domain name services, distributed synchronization, group services and so on.

5 、 Hive

Hive is a data warehouse tool based on Hadoop, which can map structured data files to a database table, provide simple sql query function, and transform sql statements into MapReduce tasks to run, which is very suitable for statistical analysis of data warehouse.

6 、 Hbase

This is the NOSQL database in the Hadoop ecosystem. Its data is stored in the form of key and value, and key is unique, so it can be used to weigh the data. Compared with MYSQL, it can store a much larger amount of data.

7 、 Kafka

Kafka is a high-throughput distributed publish and subscribe messaging system, which can handle all action flow data in consumer-scale websites, unify online and offline message processing through Hadoop's parallel loading mechanism, and provide real-time messages through clusters.

8 、 Spark

Spark is a fast and general computing engine specially designed for large-scale data processing, which has the advantages of Hadoop MapReduce, but unlike MapReduce, the intermediate output of Job can be saved in memory, so it is no longer necessary to read and write HDFS, so Spark can be better applied to MapReduce algorithms that need iteration, such as data mining and machine learning.

Thank you for reading! What about big data's learning is shared here. I hope the above content can be of some help to you, so that you can learn more knowledge. If you think the article is good, you can share it and let more people see it.

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