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Beida Jade Bird Design Training: What technologies do big data operators need to master?
With the continuous development of the Internet, more and more people hope to achieve transformation and development by learning big data technology. Today, let's take a look at what technologies you need to master to become a big data operator.

The essence of big data is the combination of data mining depth and application breadth.

Effective analysis and processing of massive data, not just a large number of data is called big data.

Three learning directions of big data: big data developers, big data architects, big data operation and maintenance normal university data developers and big data architects must be familiar with the core frameworks of mainstream big data platforms such as Hadoop, Spark and Storm.

In-depth understanding of how to write MapReduce jobs and manage workflows to complete data calculation, and be able to master important components of Hadoop ecosystem, such as Yarn, HBase, Hive, Pig, etc., by using general algorithms provided by Hadoop. So as to realize the development of platform monitoring and auxiliary operation and maintenance system.

By learning a series of developer-oriented development technologies for big data platforms such as Hadoop and Spark, I can master the tools and skills for designing and developing big data systems or platforms, and be able to engage in the deployment, development and management of distributed computing frameworks such as Hadoop and Spark cluster environment, such as performance improvement, function expansion and fault analysis.

Big data operators only need to understand the core framework of mainstream big data platforms such as Hadoop, Spark, Storm, and be familiar with the core components of Hadoop: HDFS, MapReduce, Yarn, which have the resource allocation of big data cluster environment, such as network requirements, hardware configuration, system construction, etc.

Familiar with the deployment mode, cluster construction, fault diagnosis, daily maintenance and performance optimization of various big data platforms, responsible for data collection, data cleaning, data storage, data maintenance and optimization on the platform.

Use fluent, Sqoop and other tools skillfully to load external data into the big data platform, and allocate cluster resources through management tools to realize multi-user collaborative use of cluster resources.

Space Bridge java Course Training/Discovery Through the flexible and extensible Hadoop platform, the traditional database and data warehouse system architecture is transformed, and the state of the whole process from Hadoop deployment and implementation to operation is monitored, ensuring the security, rapid response and scalability of big data business applications!