Shulou(Shulou.com)06/03 Report--

Big data is the general name of a series of technology. after years of development, big data has formed a series of links from data collection, arrangement, transmission, storage, security, analysis, presentation and application, which involve many big data jobs, which are also closely related to the Internet of things and cloud computing.

Hadoop is a distributed system infrastructure developed by the Apache Foundation. It is an open source distributed computing platform developed in Java language, which is suitable for big data's distributed storage and computing platform.

In a broad sense, big data is the product of the development of the times and technological progress. Hadoop is just a technical means to deal with big data.

Hadoop is the widely used big data platform, which is the work of big data platform developers. Hadoop is a common big data supporting platform at present. Hadoop platform provides distributed storage (HDFS), distributed computing (MapReduce), task scheduling (YARN), object storage (Ozone) and component support service (Common).

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With the continuous development of Hadoop, big data's ecology based on Hadoop is becoming more and more perfect, including Ambari, Avro, Cassandra, Chukwa, HBase, Hive, Mahout, Pig, Spark, Tez, Zookeeper and other components have been developed, these components greatly enrich the application of Hadoop itself. Gami Valley big data training, in June big data developed zero basic class, promotion class, Chengdu small class face-to-face, forecast name! With the increase in the number of components, Hadoop itself is getting heavier, so now many big data engineers prefer to use Spark because Spark is lighter and faster based on memory.

It can be understood simply as follows:

1. Big data is a high-level concept (equivalent to the concept of the Internet), while Hadoop is just an implementation of a platform framework that hosts big data (similar to various physical computer networks).

2. Big data contains a much broader connotation, and Hadoop is only part of the implementation.

What we usually call big data includes: acquisition, transmission, storage, analysis, visualization and so on, while Hadoop is more of a system platform for extraction, storage and analysis.

3. Besides Hadoop, there are many other platforms to implement big data platform.

Because Hadoop is an open source big data system platform, you listen to it the most. In addition to Hadoop platform, there are other system platforms.

Therefore, big data is not equal to Hadoop, and big data and Hadoop are not inclusive.

Big data developers can help enterprises complete the transformation of big data through the system-level service support provided by Hadoop. For developers, they only need to focus on the specific service implementation, and the system-level functions have been provided by Hadoop. Therefore, Hadoop is an important foundation for big data developers.

Learning big data definitely needs to learn Hadoop technology, the position of Hadoop in big data's technology system is very important, Hadoop is the foundation of big data's technology, and the solid degree of mastering the basic knowledge of Hadoop will determine how far to go on big data's technological road.

Due to the low hardware requirements of Hadoop, it has been used in the industry for many years. It has a sound ecology and a large number of application cases, which is very suitable for beginners (people with Java foundation) to learn. Gami Valley big data training, in June big data developed zero basic class, promotion class, Chengdu small class face-to-face, forecast name! At present, many commercial big data platforms are also based on Hadoop, so Hadoop is an important part of big data's development.

After learning Hadoop, we have to learn Spark, which is an open source cluster computing environment similar to Hadoop, which has the advantages of Hadoop MapReduce. Spark is an alternative to MapReduce, and it is compatible with HDFS and Hive, and can be integrated into the ecosystem of Hadoop to make up for the shortcomings of MapReduce. These are the two key points of big data's technical study.

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