大数据主要学习的有以下内容:

  1. Java编程技术(Java编程技术是大数据学习的基础,Java是一种强类型语言,拥有极高的跨平台能力,可以编写桌面应用程序、Web应用程序、分布式系统和嵌入式系统应用程序等,是大数据工程师最喜欢的编程工具,因此,想学好大数据,掌握Java基础是必不可少的!)
    2.Linux命令(对于大数据开发通常是在Linux环境下进行的,相比Linux操作系统,Windows操作系统是封闭的操作系统,开源的大数据软件很受限制,因此,想从事大数据开发相关工作,还需掌握Linux基础操作命令。)
  2. Hadoop(Hadoop是大数据开发的重要框架,其核心是HDFS和MapReduce,HDFS为海量的数据提供了存储,MapReduce为海量的数据提供了计算,因此,需要重点掌握,除此之外,还需要掌握Hadoop集群、Hadoop集群管理、YARN以及Hadoop高级管理等相关技术与操作!)
  3. Hive(Hive是基于Hadoop的一个数据仓库工具,可以将结构化的数据文件映射为一张数据库表,并提供简单的sql查询功能,可以将sql语句转换为MapReduce任务进行运行,十分适合数据仓库的统计分析。对于Hive需掌握其安装、应用及高级操作等。)
  4. Avro与Protobuf(Avro与Protobuf均是数据序列化系统,可以提供丰富的数据结构类型,十分适合做数据存储,还可进行不同语言之间相互通信的数据交换格式,学习大数据,需掌握其具体用法。)
    6.ZooKeeper(ZooKeeper是Hadoop和Hbase的重要组件,是一个为分布式应用提供一致性服务的软件,提供的功能包括:配置维护、域名服务、分布式同步、组件服务等,在大数据开发中要掌握ZooKeeper的常用命令及功能的实现方法)。
  5. HBase(HBase是一个分布式的、面向列的开源数据库,它不同于一般的关系数据库,更适合于非结构化数据存储的数据库,是一个高可靠性、高性能、面向列、可伸缩的分布式存储系统,大数据开发需掌握HBase基础知识、应用、架构以及高级用法等)。
    8.phoenix(phoenix是用Java编写的基于JDBC API操作HBase的开源SQL引擎,其具有动态列、散列加载、查询服务器、追踪、事务、用户自定义函数、二级索引、命名空间映射、数据收集、行时间戳列、分页查询、跳跃查询、视图以及多租户的特性,大数据开发需掌握其原理和使用方法)。
  6. Redis(Redis是一个key-value存储系统,其出现很大程度补偿了memcached这类key/value存储的不足,在部分场合可以对关系数据库起到很好的补充作用,它提供了Java,C/C++,C#,PHP,JavaScript,Perl,Object-C,Python,Ruby,Erlang等客户端,使用很方便,大数据开发需掌握Redis的安装、配置及相关使用方法)。
  7. Flume(Flume是一款高可用、高可靠、分布式的海量日志采集、聚合和传输的系统,Flume支持在日志系统中定制各类数据发送方,用于收集数据;同时,Flume提供对数据进行简单处理,并写到各种数据接受方(可定制)的能力。大数据开发需掌握其安装、配置以及相关使用方法)。
  8. SSM(SSM框架是由Spring、SpringMVC、MyBatis三个开源框架整合而成,常作为数据源较简单的web项目的框架。大数据开发需分别掌握Spring、SpringMVC、MyBatis三种框架的同时,再使用SSM进行整合操作)。
    12.Kafka(Kafka是一种高吞吐量的分布式发布订阅消息系统,其在大数据开发应用上的目的是通过Hadoop的并行加载机制来统一线上和离线的消息处理,也是为了通过集群来提供实时的消息。大数据开发需掌握Kafka架构原理及各组件的作用和使用方法及相关功能的实现)!
    13.Scala(Scala是一门多范式的编程语言,大数据开发重要框架Spark是采用Scala语言设计的,想要学好Spark框架,拥有Scala基础是必不可少的,因此,大数据开发需掌握Scala编程基础知识)!
    14.Spark(Spark是专为大规模数据处理而设计的快速通用的计算引擎,其提供了一个全面、统一的框架用于管理各种不同性质的数据集和数据源的大数据处理的需求,大数据开发需掌握Spark基础、SparkJob、Spark RDD、spark job部署与资源分配、Spark shuffle、Spark内存管理、Spark广播变量、Spark SQL、Spark Streaming以及Spark ML等相关知识)。
    15.Azkaban(Azkaban是一个批量工作流任务调度器,可用于在一个工作流内以一个特定的顺序运行一组工作和流程,可以利用Azkaban来完成大数据的任务调度,大数据开发需掌握Azkaban的相关配置及语法规则)。
    16.Python与数据分析(Python是面向对象的编程语言,拥有丰富的库,使用简单,应用广泛,在大数据领域也有所应用,主要可用于数据采集、数据分析以及数据可视化等)。

Big data mainly learns the following contents:

  1. 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. Programs, etc., are the favorite programming tools of big data engineers. Therefore, if you want to learn big data well, mastering the basics of Java is essential!)
  2. Linux commands (for big data development is usually carried out in the Linux environment, compared to the Linux operating system, the Windows operating system is a closed operating system, open source big data software is very limited, so I want to engage in big data development related To work, you also need to master Linux basic operating commands.)
  3. 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 calculations for massive amounts of data. Therefore, it needs to be mastered. In addition, Need to master Hadoop cluster, Hadoop cluster management, YARN and Hadoop advanced management and other related technologies and operations!)
  4. Hive (Hive is a data warehouse tool based on Hadoop, which can map structured data files to a database table, and provides simple SQL query functions, which can convert SQL statements into MapReduce tasks for operation, which is very suitable for data Statistical analysis of the warehouse. For Hive, you need to master its installation, application and advanced operations.)
  5. Avro and Protobuf (Avro and Protobuf are both data serialization systems, which can provide a wealth of data structure types, which are very suitable for data storage, and can also communicate data exchange 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. The functions provided include: configuration maintenance, domain name services, distributed synchronization, component services, etc., in the development of big data To master the common commands and function implementation methods of ZooKeeper).
  7. 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 highly reliable, high-performance, column-oriented, scalable Distributed storage system, big data development requires basic knowledge of HBase, applications, architecture and advanced usage, etc.).
  8. phoenix (phoenix is ​​an open source SQL engine written in Java based on JDBC API to operate HBase. It has dynamic columns, hash loading, query server, tracking, transactions, user-defined functions, secondary indexes, namespace mapping, data The characteristics of collection, row timestamp column, paging query, jump query, view, and multi-tenancy, big data development needs to master its principles and usage methods).
  9. Redis (Redis is a key-value storage system. Its appearance greatly compensates for the lack of key/value storage such as memcached. In some cases, it can complement relational databases. It provides Java, Clients such as C/C++, C#, PHP, JavaScript, Perl, Object-C, Python, Ruby, Erlang, etc., are very convenient to use. For big data development, you need to master the installation, configuration and related usage of Redis).
  10. Flume (Flume is a highly available, highly reliable, distributed system for collecting, aggregating, and transmitting massive logs. Flume supports customizing various data senders in the log system to collect data; at the same time, Flume provides The data is simply processed and written to the capabilities of various data recipients (customizable). Big data development needs to master its installation, configuration and related usage methods).
  11. SSM (SSM framework is integrated by the three open source frameworks of Spring, SpringMVC, and MyBatis, and is often used as a framework for web projects with simple data sources. Big data development requires mastering the three frameworks of Spring, SpringMVC, and MyBatis. Then use SSM for integration operations).
  12. Kafka (Kafka is a high-throughput distributed publish-and-subscribe messaging system. Its purpose in big data development and application is to unify online and offline message processing through Hadoop’s parallel loading mechanism. Provide real-time news. Big data development needs to master the principle of Kafka architecture, the role and use of each component, and the realization of related functions)!
  13. Scala (Scala is a multi-paradigm programming language. Spark, an important framework for big data development, is designed using the Scala language. If you want to learn the Spark framework well, it is essential to have a Scala foundation. Therefore, big data development needs to master Scala Basic knowledge of programming)!
    14.Spark (Spark is a fast and universal 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 basics, SparkJob, Spark RDD, spark job deployment and resource allocation, Spark shuffle, Spark memory management, Spark broadcast variables, Spark SQL, Spark Streaming, and Spark ML).
  14. Azkaban (Azkaban is a batch workflow task scheduler, which can be used to run a set of jobs and processes in a specific order within a workflow. You can use Azkaban to complete big data task scheduling. Big data development requires mastering Azkaban Related configuration and grammar rules).
  15. Python and data analysis (Python is an object-oriented programming language with a rich library, simple to use, and widely used. It is also used in the field of big data, mainly for data collection, data analysis, and data visualization, etc.).

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