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Big data is a series of processing methods for storing, calculating, counting and analyzing massive data. The amount of data processed is usually TB level, even PB or EB level data, which cannot be completed by traditional data processing methods. The technologies involved include distributed computing, high concurrent processing, high availability processing, clustering, real-time computing, etc., which bring together the popular IT technologies in the current IT field.

To learn big data, you need to master the following techniques:

Java Programming Technology

Java programming technology is the foundation of big data learning. Java is a strongly typed language with extremely high cross-platform capabilities. It can write desktop applications, Web applications, distributed systems and embedded system applications. It is the favorite programming tool for big data engineers. Therefore, it is essential to master Java foundation if you want to learn big data well!

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2. Linux commands

For big data development is usually carried out in the Linux environment, compared with the Linux operating system, Windows operating system is a closed operating system, open source big data software is very limited, therefore, want to engage in big data development related work, but also need to master Linux basic operating commands.

Hadoop

Hadoop is an important framework for big data development. Its core is HDFS and MapReduce. HDFS provides storage for massive amounts of data, and MapReduce provides computation for massive amounts of data. Therefore, you need to focus on mastering. In addition, you need to master related technologies and operations such as Hadoop cluster, Hadoop cluster management, YARN, and Hadoop advanced management!

Hive

Hive is a data warehouse tool based on Hadoop, which can map structured data files into a database table and provide simple sql query functions. It can convert sql statements into MapReduce tasks for operation, which is very suitable for statistical analysis of data warehouses. For Hive, you need to master its installation, application and advanced operation.

Avro and Protobuf

Avro and Protobuf are both data serialization systems, which can provide rich data structure types, which are very suitable for data storage. They can also exchange data formats between different languages. To learn big data, you need to master its specific usage.

6.ZooKeeper

ZooKeeper is an important component of Hadoop and Hbase. It is a software that provides consistent services for distributed applications. It provides functions including configuration maintenance, domain name service, distributed synchronization, component service, etc. In big data development, it is necessary to master the common commands and functions of ZooKeeper.

HBase

HBase is a distributed, column-oriented open source database, which is different from the general relational database, more suitable for unstructured data storage database, is a high reliability, high performance, column-oriented, scalable distributed storage system, big data development needs to master HBase basic knowledge, application, architecture and advanced usage.

8.phoenix

Phoenix is an open source SQL engine based on JDBC API operation HBase written in Java. It has the characteristics of dynamic column, hash load, query server, trace, transaction, user-defined function, secondary index, namespace mapping, data collection, row timestamp column, paging query, skip query, view and multi-tenant. Big data development needs to master its principle and use method.

Redis

Redis is a key-value storage system, its appearance largely compensates for the lack of memcached key/value storage, in some cases can play a good role in supplementing relational databases, it provides Java, C/C++, C#, PHP, Java, Perl, Object-C, Python, Ruby, Erlang and other clients, very convenient to use, big data development needs to master Redis installation, configuration and related use methods.

Flume

Flume is a highly available, highly reliable, distributed system for mass log collection, aggregation and transmission. Flume supports customization of various data senders in the log system for data collection; at the same time, Flume provides the ability to simply process data and write it to various data receivers (customizable). Big data development needs to master its installation, configuration and related usage methods.

SSM

SSM framework is composed of Spring, SpringMVC, MyBatis three open source framework integration, often as a data source simpler web project framework. Big data development needs to master Spring, SpringMVC and MyBatis frameworks respectively, and then use SSM for integration operations.

12.Kafka

Kafka is a high-throughput distributed publish-subscribe messaging system. Its purpose in big data development applications is to unify online and offline message processing through Hadoop's parallel loading mechanism, and also to provide real-time messages through clustering. Big data development needs to master the principle of Kafka architecture and the role and use of each component and the implementation of related functions!

13.Scala

Scala is a multi-paradigm programming language, big data development important framework Spark is designed in Scala language, want to learn Spark framework, have Scala foundation is essential, therefore, big data development needs to master Scala programming basics!

14.Spark

Spark is a fast and versatile computing engine designed for large-scale data processing. It provides a comprehensive and unified framework for managing the needs of big data processing for various data sets and data sources. Big data development requires knowledge of Spark fundamentals, Spark Job, Spark RDD, Spark job deployment and resource allocation, Spark shuffle, Spark memory management, Spark broadcast variables, Spark SQL, Spark Streaming, and Spark ML.

15.Azkaban

Azkaban is a batch workflow task scheduler, which can be used to run a group of tasks and processes in a specific order within a workflow. Azkaban can be used to complete task scheduling of big data. Big data development needs to master Azkaban's relevant configuration and syntax rules.

Python and Data Analysis

Python is an object-oriented programming language with rich libraries, simple to use, widely used, and also applied in the field of big data, mainly used for data acquisition, data analysis, and data visualization. Therefore, big data development requires learning certain Python knowledge.

Only when you have completed the above technologies can you be regarded as a big data development talent. If you are really engaged in big data development-related work, your work will be more confident. Promotion and salary increase will not be a problem.

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