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

Although life is not a wasted road, the new year comes, will still be the original knowledge, people's worth is there, no matter how much trouble, will not get a better offer. Therefore, it is very important to learn more when you are young, to find your own shortcomings, and to check and fill the gaps. * * what the editor brings you today is absolute practical information! Here are the pits I have climbed over the years. It is quite comprehensive in the development of big data. Don't talk too much nonsense, just go to the practical information!

1. Java programming technology.

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

Here I still want to recommend the big data Learning Exchange Group I built myself: 529867072, all of them are developed by big data. If you are studying big data, the editor welcomes you to join us. Everyone is a software development party. Irregularly share practical information (only related to big data software development), including the latest big data advanced materials and advanced development tutorials sorted out by myself. Welcome to join us if you want to go deep into big data.

2. Linux command

The development of big data is usually carried out in the Linux environment. Compared with the Linux operating system, the Windows operating system is a closed operating system, and the open source big data software is very limited. Therefore, if you want to engage in big data development related work, you need to master the basic Linux operation commands.

3 、 Hadoop

Hadoop is an important framework developed by big data, its core is that HDFS and MapReduce,HDFS provide storage for massive data, MapReduce provides computing for massive data, so we need to focus on mastering, in addition, we also need to master Hadoop cluster, Hadoop cluster management, YARN and Hadoop advanced management and other related technologies and operations!

4. Avro and Protobuf

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

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. It is necessary to master the installation, application and advanced operation of Hive.

6 、 HBase

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

7 、 Redis

Redis is a key-value storage system, which largely compensates for the deficiency of key/value storage such as memcached, and can supplement the relational database on some occasions. It provides clients such as Java,C/C++,C#,PHP,Java,Perl,Object-C,Python,Ruby,Erlang, which is very convenient to use. Big data's development needs to master the installation, configuration and related usage of Redis.

8 、 ZooKeeper

ZooKeeper is an important component of Hadoop and Hbase. It is a software that provides consistency services for distributed applications. The functions provided include configuration maintenance, domain name service, distributed synchronization, component services and so on. In the development of big data, it is necessary to master the common commands of ZooKeeper and the implementation methods of its functions.

9 、 Flume

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

10 、 Azkaban

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

11 、 SSM

The SSM framework is integrated by three open source frameworks, Spring, SpringMVC and MyBatis, and is often used as a framework for web projects with simple data sources. The development of big data needs to master the three frameworks of Spring, SpringMVC and MyBatis respectively, and then use SSM for integration operation.

12 、 Kafka

Kafka is a high-throughput distributed publish and subscribe messaging system. The purpose of big data's development and application is not only to unify online and offline message processing through Hadoop's parallel loading mechanism, but also to provide real-time messages through clusters. The development of big data needs to master the principle of Kafka architecture and the function and usage of each component as well as the realization of related functions.

13. Python and data analysis

Python is an object-oriented programming language with rich libraries, easy to use and widely used. It is also used in the field of big data, mainly for data acquisition, data analysis and data visualization. Therefore, big data needs to learn some knowledge of Python.

14 、 phoenix

Phoenix is an open source SQL engine based on JDBC API operation HBase written in Java, which has the characteristics of dynamic column, hash loading, query server, tracking, transaction, user-defined function, secondary index, namespace mapping, data collection, row time stamp column, paging query, jump query, view and multi-tenant. Big data development needs to master its principle and usage.

15 、 Scala

Scala is a multi-paradigm programming language. Big data designed the important framework Spark using Scala language. If you want to learn the Spark framework well, it is essential to have the foundation of Scala. Therefore, big data needs to master the basic knowledge of Scala programming!

16 、 Spark

Spark is a fast and general computing engine specially designed for large-scale data processing. It provides a comprehensive and unified framework for managing the needs of big data processing of different datasets and data sources. Big data development needs to master the basic knowledge of Spark, SparkJob, Spark RDD, spark job deployment and resource allocation, Spark shuffle, Spark memory management, Spark broadcast variables, Spark SQL, Spark Streaming and Spark ML.

Conclusion

Big data is a hot IT subject at that time, the market is very hot, whether it is large companies like Alibaba and Baidu, or small and medium-sized enterprises attach great importance to, or even the first technology into the national strategy, the government support is strong, support a lot!

In the face of such an environment, big data related positions have high salaries and good employment prospects. So there are more people with lofty ideals to participate, but to change careers still need to be cautious, each industry has the requirements of each industry, according to their own interests and hobbies appropriate understanding, consider carefully before making a choice, do not blindly follow the trend. Finally, I wish you all can get an ideal job in the new year.

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