Big data is a series of processing methods for storing, calculating, statistics, and analyzing massive amounts of data. The amount of data processed is usually TB level, or even PB or EB level data, which is beyond the reach of traditional data processing methods. Completed, the technologies involved include distributed computing, high concurrency processing, high availability processing, clustering, real-time computing, etc., which brings together the most popular IT technologies in the current IT field.
What do you need to learn about big data?
1. Java programming technology
Java programming technology is the basis for 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 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
Big data development is usually carried out in the Linux environment. Compared with the Linux operating system, the Windows operating system is a closed operating system, and open source big data software is very limited. Therefore, if you want to engage in big data development For related work, you also need to master the basic operating commands of Linux.
3. Hadoop
Hadoop is an important framework for big data development. Its core is HDFS and MapReduce. HDFS provides storage for massive data, and MapReduce provides calculation for massive data. Therefore , need to focus on mastering, in addition, you also need to master related technologies and operations such as Hadoop cluster, Hadoop cluster management, YARN and Hadoop advanced management!
4. Hive
Hive is based on Hadoop A data warehouse tool that can map structured data files into a database table and provide simple SQL query functions. It can convert SQL statements into MapReduce tasks for running, which is very suitable for statistical analysis of data warehouses. For Hive, you need to master its installation, application and advanced operations.
5. Avro and Protobuf
Avro and Protobuf are both data serialization systems that can provide rich data structure types and are very suitable for data storage and communication between different languages. Data exchange format, 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 big data development, you must master ZooKeeper's common commands and implementation methods of functions.
7. HBase
HBase is a distributed, column-oriented open source database. It is different from general relational databases and is more suitable for unstructured data storage. It is a high-level database. A reliable, high-performance, column-oriented, scalable distributed storage system. Big data development requires mastering the basic knowledge, applications, architecture and advanced usage of HBase.
8.phoenix
phoenix is an open source SQL engine written in Java based on the JDBC API to operate HBase. It has dynamic columns, hash loading, query server, tracking, transactions, and user customization. Big data development requires mastering the principles and usage of functions, secondary indexes, namespace mapping, data collection, row timestamp columns, paging queries, jump queries, views and multi-tenant features.
9. Redis
Redis is a key-value storage system. Its emergence has greatly compensated for the shortcomings of key/value storage such as memcached. In some cases, it can play a role in relational databases. A very good supplement. It provides Java, C/C, C#, PHP, JavaScript, Perl, Object-C, Python, Ruby, Erlang and other clients. It is very convenient to use. Big data development requires mastering the installation and configuration of Redis. and related usage methods.
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. , used to collect data; at the same time, Flume provides the ability to simply process data and write to various data recipients (customizable). Big data development requires mastering its installation, configuration and related usage methods.
11. SSM
The SSM framework is an integration of three open source frameworks: Spring, SpringMVC, and MyBatis. It is often used as a framework for web projects with relatively simple data sources. Big data development requires mastering the three frameworks of Spring, SpringMVC, and MyBatis respectively, and then using SSM for integration operations.
12.Kafka
Kafka is a high-throughput distributed publish-subscribe messaging system. Its purpose in big data development and application is to unify online processes through Hadoop’s parallel loading mechanism. And offline message processing is also to provide real-time messages through the cluster. Big data development requires mastering the principles of Kafka architecture, the functions and usage of each component, and the implementation of related functions!
13.Scala
Scala is a multi-paradigm programming language, big data development The important framework Spark 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 requires mastering the basic knowledge of Scala programming!
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 of different natures. Data development requires mastering 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 and other related knowledge.
15.Azkaban
Azkaban is a batch workflow task scheduler that can be used to run a set of jobs and processes in a specific order within a workflow. Azkaban can be used to complete large tasks. Data task scheduling and big data development require mastering the relevant configuration and syntax rules of Azkaban.
16.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 and can mainly be used for data collection. , data analysis and data visualization, etc. Therefore, big data development requires learning certain Python knowledge.
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