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

The core curriculum developed by big data is the Hadoop framework, and it can almost be said that Hadoop is developed by big data. This framework is similar to the SSH/SSM framework of Java application development. It is an open source Java framework used by the Apache Foundation or other Java open source community groups. Kodo big data big data is here to show you.

This is the truth that the Java language is king, and the core code of Java is open source, which has been tested by the global talent, research and development, so Java is the most tested language, and anyone can learn the core technology of Java and use the core technology to develop a system like android and a framework like Hadoop. If you compare the world of programming to a tree, then Java is the root, and frameworks like SSH and Hadoop make it spread out.

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.

As big data's development engineer is currently the hottest major in IT training, big data's technical talents are the pioneers of the intelligent revolution and the most direct beneficiaries of the intelligent age. So many important specialties must be explained in detail and thoroughly, mainly in the Hadoop ecosystem, introducing all the technologies currently used by big data's application-level development engineers in their work. It is recommended that you should have some learning experience in Java basic grammar and framework before studying big data as a development engineer.

Codo big data's Zero Foundation course includes two parts of java+ big data development, while the improvement course for friends with java development experience only includes big data. Because according to the previous introduction, you should know that big data's study needs a certain java foundation.

Open source Hadoop big data development platform

Hadoop is a software framework capable of distributed processing of large amounts of data. Hadoop processes data in a reliable, efficient and scalable way. The reason why users can easily develop and run application data that deal with massive data on hadoop is that hadoop has the advantages of high reliability, high expansibility, high efficiency, high fault tolerance and so on.

Hadoop big data ecosystem:

Distributed file system-HDFS

When it comes to the hadoop file system, the first thing that comes to mind is HDFS (Hadoop Distributed File System). HDFS is the main file system of hadoop, a platform for Hadoop to store data, and a distributed storage system based on the network. Hadoop integrates with other file systems, and hadoop's file system is an abstract concept, and HDFS is just one of them.

Distributed Computing Framework-MapReduce

MapReduce is a programming model and a platform for Hadoop to process data. Parallel operations for large datasets (larger than 1TB). The concepts "Map" and "Reduce", and their main ideas, are borrowed from functional programming languages, as well as features borrowed from vector programming languages. It greatly facilitates programmers to run their programs on distributed systems without distributed parallel programming.

Distributed open source database-Hbase

HBase-Hadoop Database,HBase is a distributed, column-oriented, open source database. It is suitable for unstructured data storage and retains multiple time versions of the data. Hbase greatly expands the data processing and application of Hadoop.

Big data development platform module ecosphere

Hive

Hive is a Hadoop-based data warehouse tool that handles structured SQL query functions. The structured data file can be mapped to a database table, and simple sql query functions can be provided. Sql statements can be converted into MapReduce tasks to run and submitted to the cluster for execution. Its advantage is that the learning cost is low, simple MapReduce statistics can be quickly realized through SQL-like statements, and there is no need to develop special MapReduce applications or Java programming, so it is very suitable for statistical analysis of data warehouse.

When learning Hive, it is necessary to master the DDL and DML in Hive QL; the definition of table, the export of data and the mastery of commonly used query sentences are the basis of big data's statistical analysis. Learn to program for Hive: use Java API to operate Hive and develop Hive UDF functions. Mastering some advanced features of Hive can greatly improve the execution efficiency of Hive. In the process of optimization, we can analyze it with the help of the execution plan. When learning Hive, we need to pay attention to Hive performance optimization is the most important link in production, how to solve the data tilt is the key; sorting out the relationship between Hive metadata tables can also improve the ability to grasp Hive.

Zookeeper coordinates the work of all modules in the Hadoop ecosystem.

In English sense, Hadoop is a baby elephant, Hive is a bee, pig is a pig, and Zookeeper is an animal keeper. Then it is obvious that the role of Zookeeper is to coordinate services for distributed applications and to provide consistent services for each module.

Data Import and Export Framework Sqoop

Sqoop is an open source tool, which means elephant husband, that is, people who feed elephants. It is mainly used in Hadoop (Hive) and traditional databases (mysql, postgresql...). Data transfer between can import the data from a relational database into the HDFS of Hadoop, and also import the data from HDFS into the relational database. Big data Learning Exchange Group: 251956502

Learning objectives:

1. Understand what Sqoop is, what it can do, and its architecture

two。 Ability to deploy Sqoop environment

3. Master the use of Sqoop in production

4. You can use Sqoop for ETL operations.

Scala programming development

Scala is a functional object-oriented language, similar to RUBY and GROOVY. It seamlessly combines many unprecedented features to form a multi-paradigm language, in which the high-level concurrency model is suitable for big data development. While running on the JAVA virtual machine at the same time.

Spark

Spark is the most popular big data processing framework at present, which is famous for its simplicity, easy to use and excellent performance. Rich program interfaces and library files also make Spark a necessary tool for rapid data processing and distributed machine learning in the industry.

* expand skills:

Python Development Foundation, data Analysis and data Mining

Learn the data mining tool Sklearn, familiar with data mining naive Bayesian algorithm and data mining SVM classification algorithm, and finally use Sklearn to achieve Bayesian and SVM algorithm.

Storm big data distributed real-time computing

Storm is a framework for distributed data processing. Storm can easily write and expand complex real-time computing in a computer cluster. Storm is used for real-time processing, just like Hadoop for batch processing. If MapReduce reduces the complexity of parallel batch processing, Storm reduces the complexity of real-time processing.

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