Description: Big Data era, the traditional operation and maintenance of large data-dimensional transport to upgrading common, is also a good opportunity. If you want to learn the system operation and maintenance of large data, personally recommend share class communications giant operation and maintenance of large coffee:
https://url.cn/5HIqOOr
, mainly real strong, high gold content, high concentration, there are six thematic +2 + Tencent large projects cloud server, delivering thousands of live ammunition of large data cluster operation and maintenance experience.
Course Introduction:
It is specifically designed for IT operation and maintenance personnel of high-end large data program, may also be present only one! Course content extract knowledge from more than 100 recruitment requirements, and then invite a few large data operation and maintenance experts to discuss the annual salary of 60W + polished. Traditional operation and maintenance personnel busy work ~ ~ ~ tired ~ bitter but low wages, a veteran of many years of operation and maintenance is often better to pay just graduated a couple of years of farming code. This course is intended to take the bonus potential of big data to help you seize the opportunity to win high-paying, professional upgrading gorgeous!
Courses to telecom operators real project-oriented, with large enterprise data operation and maintenance of practical application scenarios, step by step with the students master the operation and maintenance of large data various technical aspects. By "big data platform from zero construction" combat, so that students have the ability to help companies achieve big data platform from scratch; by "combat operation and maintenance of large-scale cluster" real, so that students have the ability to safeguard the stability of maintaining large clusters of large data, effective and safe. Courses in considering the content of the coverage the same time, places great emphasis on practicality, to let the students have learned that is used to effectively solve the actual problem at work, refused to wasted effort and skill false. Full and detailed documentation, much of the production line from the practice environment of thousands of nodes, maybe you can come up with a big data operation and maintenance "human resource configurations."
Course highlights:
Real strong: in order to address the actual problem-oriented. 0 Big Data platform construction project, to address the urgent problem of corporate big data platform from scratch. Cluster operation and maintenance of large-scale combat, how to solve the business running smoothly and maintaining large data platform.
High gold content: The Cloudra Manager to build enterprise-class big data platform, big data operation and maintenance experience to teach thousands of nodes, solve one hundred billion level big data clusters on production line.
A high degree of focus: focus on large data operation and maintenance, planning courses cover a large data clusters, cluster deployment, security cluster, cluster monitoring, vessel of clusters, cluster operation and maintenance, operation and maintenance of large data train professionals.
For the crowd:
1.IT operation and maintenance personnel to enhance the high salaries
2. network management / technical support transformation transformation salary increase
3.Leader / architect expand technology stack
4. College / undergraduate students to easily join the Big Data
employment position:
1. Big Data Operation & Maintenance Engineer
2. Big Data Platform Architect
3. Big Data platform operation and maintenance
Course Outline:
The first chapter of large data operation and maintenance Liberal
1. Large data overview and introduction of ecological technology
2. How big data and other operation and maintenance division of labor cooperation
3. What are the operation and maintenance of large data necessary skills needed to master
4. How to become a big data-paying operation and maintenance personnel
Chapter Two Big Data Cluster Programming
1. Network Planning
1.1 Room zoning
Room three network architectures 1.2
1.3 network bandwidth planning (Gigabit, Gigabit)
1.4 host adapters, bond mode
2. Cluster planning
2.1 trunking service plan
2.2 cluster node planning
2.2.1HDFS cluster nodes planning
2.2.2HBase cluster nodes planning
2.2.3Kafka cluster nodes planning
2.2.4Zookeeper node planning
2.2.5YARN node planning
2.2.6ElasticSearch node planning
2.3 storage plan
2.3.1Raid planning
2.3.2 Multi-disk layout
Chapter Three Big Data Cluster Setup
1. cluster installation deployment
1.1Ambari + hdp automated deployment
1.2CM + cdh automated deployment
1.3Hadoop manually install deployment
2. cluster deployment platform of choice
Ali Cloud 2.1
2.2EC2
2.3 physical server
3. Big Data technology component deployment
3.1Zookeeper cluster installation
3.2HDFS cluster installation
3.3YARN cluster installation
3.4Hive client installation
3.5HBase cluster installation
3.6Kafka cluster installation
3.7Spark cluster installation
3.8Flink cluster installation
3.9 Interface machine / machine installation ramp
4. Core Architecture technology components
4.1HDFS architecture
4.2YARN architecture
4.3HBase architecture
4.4Kafka architecture
Chapter Four large data cluster security
1.HDFS ACL storage access control
2. Resource Queue access control
3.HDFS Sentry Access Control
4.vpn access control
The cloud desktop access control
Chapter fifth largest cluster monitoring data
1. cluster level monitoring
1.1 Cluster cpu load
1.2 cluster disk IO load
1.3 cluster network IO load
1.4HDFS IO load
1.5 Cluster memory load
2.YARN monitoring
2.1ResourceManager health
2.2NodeManager health
2.3JobHistory Server Health
2.4 Application Monitoring
2.5 vessel monitoring
2.6JVM monitoring
2.7RPC monitoring
Job Monitor 2.8
2.9 resource queue monitoring
3.HDFS monitoring
3.1 Monitoring capacity
3.2DataNode read and write monitoring
3.3 Transaction Monitoring
3.4 Edit Log Monitoring
3.5Rpc monitoring
3.6JVM stack monitoring
4.Kafka monitoring
4.1Broker monitoring
4.2topic partition monitoring
4.3IO monitoring
5.Zookeeper monitoring
IAAS layer indicators to monitor progress 5.1
5.2 Health Monitoring
5.3 connection monitoring
5.4 Monitoring request
5.5 Monitoring Packet
5.6JVM monitoring
6.HBase monitoring
6.1regionserver regional monitoring
6.2 read and write requests monitor
6.3 Event Monitoring
6.4 Status Monitoring
6.5JVM monitoring
6.6 host node key indicators to monitor
Chapter Six large data container technology
1.Docker container technology
The principle 1.1Docker
1.2Docker installation and deployment
1.3Docker container management
1.4Docker mirror and Warehouse Management
1.5Spark ON Docker Cluster Setup
2.Kubernetes (k8s) container Technology
2.1k8s Quick Start
2.2k8s System Architecture
2.3k8s basis Component Description
2.4k8s install basic services
2.5k8s distributed installation
2.6k8s Nginx deployment
3. The container of large data Practice
3.1 Flink calculated based on large data stream kubernetes (k8s) scheduling
3.2Docker + k8s container technology practice landing in the big data application services
3.3Docker + k8s deployment, monitoring practice
Project: Construction of big data platform from 0
1. Big Data platform preliminary investigation
1.1 History of the total data
1.2 data growth per day
1.3 TTL data
2. Cluster Hardware Planning
2.1 consider the overall plan
2.1.1 cluster size control factor
1) based on the amount of data to calculate the total disk
2) calculated based on the total amount of memory blocks NameNode
3) based on the number of assignments and performance computing clusters
Construction of considerations 2.1.2 Cluster
1) Construction of HA HA Cluster
2) physical machine, cloud host
3) deployment options: a native cluster, CDH cluster, hdp cluster
2.2 cluster hardware options
2.2.1 master node configuration
2.2.2 from the node configuration
2.2.3CPU Configuration
2.2.4Core and memory configuration
2.2.5 Disk Configuration
3. cluster nodes planning
3.1 trunking service plan
3.2 cluster node planning
3.2.1HDFS node planning
3.2.2HBase node planning
3.2.3Kafka node planning
3.2.4YARN node planning
3.2.5Zookeeper node planning
3.2.6ElasticSearch node planning
3.3 clustered storage plan
4. Big Data platform directory planning
4.1HDFS planning directory
4.2linux os directory planning
4.3linux hostname planning
4.4 Planning temporary directory
5. Network Planning
5.1 Select room
5.2 network bandwidth planning
5.3 host adapters, planning
6. Big Data platform to build
6.1 Select the cluster deployment platform
6.2 Select the cluster deployment
6.3 platform to build large data
6.4 Interface Deployment
7. data migration to large data platform
7.1 file data migration big data platform
7.2 General tree database data migration platform
7.3 Data consistency verification
8. Big Data platform maintenance and management
8.1 cluster start and stop
8.2 Each cluster maintenance operations process
8.3 balancing operation data
8.4 Cluster daily operation and maintenance
8.5 Big Data platform access control
Project II: large-scale cluster operation and maintenance practices
1. The basic operation and maintenance of large data clusters
1.1 cluster start and stop
1.2 Each cluster maintenance operations process
1.3 data balancing operation
1.4 Cluster daily operation and maintenance
2. Big Data clusters scaling capacity
Add and delete nodes 2.1HDFS
Add and delete nodes 2.2YARN
Add and delete nodes 2.3HBase
Add and delete nodes 2.4Kafka
3. Big Data clusters inspection
3.1HDFS patrol ideas and tips
3.2YARN patrol ideas and tips
3.3HBase patrol ideas and tips
3.4Zookeeper patrol ideas and tips
3.5Kafka patrol ideas and tips
4. Large data cluster production line problem solving
4.1 data processing production line environment deferred location
4.2 locate the root cause slow job execution
4.3 Troubleshooting tilt job data
4.4hive storage root cause delay positioned
4.5HBase data loss recovery disk failure
4.6HBase data mistakenly deleted data recovery
Resources application 4.7Spark environmental problems caused by the positioning less than
The large-scale cluster data migration
5.1 Data Migration
5.2 Data Migration ago cluster ready
5.3 large-scale data migration process
5.4 Data Validation