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I. Big Data Technology Basics

1. Linux operation Basics

• Introduction and installation of Linux
• Common Linux commands-File Operations
• Common Linux commands-user management and permissions
• Common Linux commands-system management
• Common Linux commands-password-free login configuration and Network Management
• Install common software on Linux
• Linux local Yum source configuration and Yum Software Installation
• Linux Firewall Configuration
• Linux advanced text processing commands: Cut, sed, awk
• Linux scheduled task crontab

2. shell programming

• Shell programming-basic syntax
• Shell programming-Process Control
• Shell programming-Functions
• Shell programming-comprehensive case-automated deployment script
  Big Data Learning Group 142973723

3. Memory Database redis

• Introduction to redis and nosql
• Redis Client Connection
• Redis string-type data structure operations and applications-object Cache
• List-type data structure operations and application cases of redis-task scheduling queue
• Redis hash and Set Data Structure operations and application cases-shopping cart
• Redis sortedset data structure operations and application cases-ranking list

4. Distributed Coordination Service zookeeper

• Introduction and application scenarios of zookeeper
• Zookeeper cluster installation and deployment
• Zookeeper data nodes and command line operations
• Java client basic operations and event listening for zookeeper
• Zookeeper core mechanism and data nodes
• Zookeeper application case-Distributed Shared Resource lock
• Zookeeper application case-server online/offline dynamic awareness
• Data Consistency principle of zookeeper and Leader Election Mechanism

5. Enhanced Java advanced features

• Java multithreading Basics
• Java synchronization keywords
• Java concurrent packet thread pool and Its Application in open source software
• Java concurrent packet messaging team and Applications in open source software
• Java JMS technology
• Java Dynamic proxy reflection

6. Lightweight RPC framework development

• RPC principle Learning
• NIO principle Learning
• Netty common API Learning
• Lightweight RPC framework requirement analysis and Principle Analysis
• Lightweight RPC framework development

Ii. Offline Computing System

1. hadoop Quick Start

• Hadoop background
• Distributed System Overview
• Offline data analysis process
• Cluster creation
• Cluster usage

2. HDFS Enhancement

• HDFS concepts and features
• Shell (command line client) operations of HDFS
• HDFS Working Mechanism
• Working Mechanism of namenode
• Java API operations
• Case 1: Develop shell collection scripts

3. mapreduce details

• Customize the RPC framework of hadoop
• Mapreduce programming specifications and Examples
• Mapreduce program running mode and Debug Method
• Internal Mechanism of mapreduce program running mode
• Main workflow of mapreduce computing framework
• Custom Object serialization Method
• Mapreduce programming case

4. mapreduce Enhancement

• Mapreduce sorting
• Custom partitioner
• Mapreduce combiner
• Mapreduce Working Mechanism

5. mapreduce practice

• Maptask concurrency mechanism-file Slicing
• Maptask concurrency settings
• Inverted index
• Mutual friends

6. Federation introduction and hive usage

• Hadoop ha Mechanism
• HA cluster installation and deployment
• Cluster O & M test-datanode dynamic online/offline
• Namenode status switch management for cluster O & M Testing
• Balance of data blocks for cluster O & M Testing
• Changes in HDFS-API under HA
• Hive Introduction
• Hive Architecture
• Install and deploy hive
• Initial use of hvie

7. Hive enhancement and flume Introduction

• HQL-DDL basic syntax
• HQL-DML basic syntax
• Hive join
• Hive parameter configuration
• Hive user-defined functions and transform
• Hive executes hql instance analysis
• Notes on hive Best Practices
• Hive Optimization Strategy
• Hive case studies
• Flume Introduction
• Install and deploy Flume
• Case: Collect directories to HDFS
• Case: collect files to HDFS

Iii. Stream computing

1. From getting started to proficient in Storm

• What is storm?
• Storm Architecture Analysis
• Storm Architecture Analysis
• Storm programming model, tuple source code, concurrency Analysis
• Storm wordcount case and common API analysis
• Storm cluster deployment practices
• Storm + Kafka + redis business indicator calculation
• Storm source code download and compilation
• Strom cluster startup and source code analysis
• Storm task submission and source code analysis
• Storm data sending Process Analysis
• Storm Communication Mechanism Analysis
• Storm message Fault Tolerance Mechanism and source code analysis
• Storm multi-Stream Project Analysis
• Compile your own streaming task execution framework

2. Storm upstream and downstream and architecture Integration

• What is a message queue?
• Kakfa Core Components
• Kafka cluster deployment practices and common commands
• Kafka Configuration File Sorting
• Kakfa javaapi Learning
• Analysis of Kafka file storage mechanism
• Apsaradb for redis basic and standalone environment deployment
• Redis data structure and typical cases
• Flume Quick Start
• Flume + Kafka + storm + redis Integration

Iv. memory computing system spark

1. Scala Programming

• Scala Programming
• Scala-related software installation
• Scala basic syntax
• Scala methods and functions
• Scala functional programming features
• Scala array and set
• Scala programming exercise (single-host wordcount)
• Scala object-oriented
• Scala pattern matching
• Actor Programming
• Option and partial function
• Practice: Concurrent wordcount of actor
• Kerihua
• Implicit conversion

2. akka and RPC

• Akka concurrent programming framework
• Practice: RPC Programming Practice

3. Spark Quick Start

• Spark Introduction
• Spark Environment Construction
• RDD Introduction
• RDD conversion and action
• Practice: RDD Comprehensive Exercises
• RDD advanced Operator
• Custom partitioner
• Practice: website visits
• Broadcast variable
• Practice: Calculate geographic locations based on IP addresses
• Custom sorting
• Use jdbc rdd to import and export data
• Detailed description of worldcount Execution Process

4. RDD details

• RDD dependency
• RDD Cache Mechanism
• RDD checkpoint Mechanism
• Spark task Execution Process Analysis
• RDD Stage Division

5. Spark-SQL application

• Spark-SQL
• Spark combined with hive
• Dataframe
• Practice: spark-SQL and dataframe Cases

6. sparkstreaming Application Practice

• Introduction to spark-streaming
• Spark-streaming Programming
• Practice: stagefulwordcount
• Flume combined with spark streaming
• Combine Kafka with spark streaming
• Window functions
• Elk technology stack Introduction
• Install and use elasticsearch
• Storm Architecture Analysis
• Storm programming model, tuple source code, concurrency Analysis
• Storm wordcount case and common API analysis

7. Spark core source code analysis

• Spark Source code compilation
• Spark remote debug
• Source code analysis of spark job submission Line Process
• Spark communication process source code analysis
• Source code analysis of sparkcontext creation process
• Source code analysis of the communication process between driveractor and clientactor
• Source code analysis of the executor startup process by worker
• Executor registration process source code analysis to driveractor
• Executor registration process source code analysis to driver
• Dagscheduler and taskscheduler source code analysis
• Shuffle process source code analysis
• Task execution process source code analysis

V. Machine Learning Algorithms

1. Python and numpy Libraries

• Machine Learning Overview
• Machine Learning and Python
• Python-Quick Start
• Python-Data Types
• Python-process control statements
• Python language-function usage
• Python-modules and packages
• Phthon language-object-oriented
• Python Machine Learning Algorithm Library-numpy
• Mathematical knowledge required for Machine Learning-Probability Theory

2. Common Algorithm Implementation

• KNN classification algorithm-algorithm principles
• KNN classification algorithm-code implementation
• KNN classification algorithm-Case Study of hand writing Recognition
• Lineage regression classification algorithm-algorithm principle
• Lineage regression classification algorithm-Algorithm Implementation and demo
• Naive Bayes classification algorithm-algorithm principle
• Naive Bayes classification algorithm-Algorithm Implementation
• Naive Bayes classification algorithm-spam recognition application case
• Kmeans clustering algorithm-algorithm principle
• Kmeans clustering algorithm-Algorithm Implementation
• Kmeans clustering algorithm-application of geographic location Clustering
• Decision tree classification algorithm-algorithm principles
• Decision tree classification algorithm-Algorithm Implementation

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Learn the big data technology course and learn it with confidence. Let's get started.