Chengdu Big Data Hadoop and Spark technology training course

• Chengdu Big Data Hadoop and Spark technology training course

China Information Training Center has launched the Big Data Technology architecture and application of practical training courses, through professional big data Hadoop and Spark technology architecture system and the industry real case to comprehensively improve the work of big Data engineer, development and design staff, Designed to nurture professional big data Hadoop and spark technology architecture experts to better serve the development and implementation of big data projects in various industries.

- recent Open Class arrangement: (Nationwide Tour classes)

August 21--Dalian, August 23

September 23--September 25 Beijing

October 16--Chengdu, October 18

November 27--November 29 Shenzhen

December 24--December 26 Guangzhou

January 27--Hangzhou, January 29

Admissions Target:

Application Development Engineer for big Data Hadoop and spark technology

Big Data analytics and mining engineer

Big Data cluster operation and maintenance engineer

IT managers for big data projects

Consulting staff for Big Data project planning

Enthusiasts interested in Hadoop and spark big Data technology

Plan to launch Big Data Project and enterprise information technology and management personnel in various industries with big data application demand

Have a certain Java and Linux Foundation is preferred.

Certificate of Training: "Senior architect of Big Data Hadoop development" issued by China Information Training Center .

Charging Standard: 5800 Yuan / people

Open Course Training outline: (internal training program can be customized)

Schedule	Training modules	Training Essentials
First day Morning	First, the Big Data technology basic introduction	1. Background and history of big data generation 2. The relationship between big data and cloud computing 3. Big Data application requirements and potential value analysis 4. Industry's latest big data technology development trend and application trends 5. Technology selection and architecture design of Big Data project 6. Application and application of e-commerce, manufacturing, retail and wholesale industry, telecom operators, internet finance, Online Banking, e-government, mobile Internet, education and information industry in the era of "Internet +"
	Second, the industry mainstream big data technology products and project solutions	7. Introduction to major data solutions at home and abroad 8. Comparison of current big data solutions with traditional database scenarios Analysis of 9.Apache Big Data platform scheme Analysis of 10.CDH Big Data platform scheme Analysis of 11.HDP Big Data platform scheme 12. Analysis of open source Big data ecosystem platform
	Third, Hadoop and spark big data processing platform	13.Hadoop development process and practical application of industry 14.Hadoop Big Data Platform architecture, and the working principle and mechanism of PB-based large-scale processing Anatomy of the core components of 15.Hadoop 16.Spark development process and practical application in the industry 17.Spark Real-time large data processing platform architecture, and the principle and mechanism of large memory data processing Anatomy of the core components of 18.Spark
First day Afternoon	Four, large  and distributed message subscription system	19.flume-ng Data flow model, platform architecture, cluster deployment and Configuration application in the system Application introduction, platform architecture, cluster deployment and Configuration application in 20.Kafka distributed message subscription system 21.Scribe Distributed Log Collection system introduction, working principle, platform architecture, cluster deployment and Configuration Application combat 22.ZooKeeper Distributed Coordination Service system working principle, platform architecture, cluster deployment and Configuration Application combat
	Five, big data distributed storage System	23. Introduction to Distributed File System HDFs Master-Slave platform architecture and working principle of 24.HDFS system 25.HDFS Core Technology Explained 26.HDFS Application Development Combat installation, deployment, configuration, and performance optimization techniques for 27.HDFS clusters 28. Distributed key-value Storage System introduction, platform architecture, core technology and application development Project case analysis of 29.PB and big data storage systems
	VI, Big Data mapreduce and yarn parallel processing platform	30.MapReduce Parallel Computing Model 31.MapReduce job execution and scheduling technology 32. How the second-generation Big Data computing framework yarn works and the Dag parallel execution mechanism 33.MapReduce deployment of application development environments and development of big data parallel processing applications 34.MapReduce advanced programming techniques and performance optimization practices 35.MapReduce and Yarn Project case Practice
Next day Morning	Seven, big data spark real-time processing platform	36. Memory computing model and real-time processing technology Introduction 37.Spark distributed real-time processing framework and working principle The platform architecture of 38.Spark cluster and analysis of its ecosystem components 39.Spark SQL Application Practice 40.Spark Streaming Application Practice 41.mlib/mlbase Real-time Machine learning application Practice Application practice of 42.GraphX real-time graph data processing Installation deployment and configuration optimization for 43.Spark real-time processing cluster 44.Spark programming development and application of the actual combat 45.Spark and Hadoop Docking Integration solution Practice
	The storm flow data processing platform	46.Storm Streaming system introduction, platform architecture and how it works 47.Storm cluster installation deployment and configuration optimization 48.Storm Log Analysis Project Application combat
Next day Afternoon	Nine, HBase distributed database management system	Introduction of 49.NoSQL Database and Newsql database technology and its application in semi-structured and unstructured big data Introduction to 50.HBase Distributed database, data model, and how it works Analysis of platform architecture and key technologies of 51.HBase distributed database cluster 52.HBase Application project development skills, and client development 53.HBase table design and data manipulation and database management API calls Installation deployment and configuration optimization for 54.HBase clusters Operation and maintenance of 55.HBase cluster and monitoring management
	X. Cassandra Data Management System	Application introduction of 56.CASSANDRA data storage Management System 57.Cassandra cluster platform architecture and core Key technologies 58.Cassandra consistent hashing algorithm and data object distribution strategy Installation deployment and configuration optimization for 59.Cassandra clusters 60.Cassandra Application Development Combat
Third Day Morning	XI. Memory Database management system cluster	Application introduction of 61.Impala Real-time query system 62.Impala real-time query system platform architecture, core key technology analysis Deployment and application development practice of 63.Impala real-time query system 64.Redis Memory Database Introduction, and Industry application case 65.Redis Memory Database cluster architecture and core technology analysis 66.Redis cluster installation deployment and application development combat
	12. Large Data Warehouse hive cluster platform	67. Hadoop-based large distributed data Warehouse fundamentals and application practices in the industry 68. Spark-based real-time Data Warehouse cluster basics, and application practices in the industry Introduction to 69.Hive Big Data Warehouse and application introduction Analysis of platform architecture and core technology of 70.Hive Data Warehouse cluster 71.Hive Server working principle and application skills installation, deployment and configuration optimization of 72.Hive Data Warehouse cluster 73.Hive Application Development Tips 74.Hive QL Definition and application 75.Hive Data Warehouse tables and table partitioning, table operations, data import and export, client manipulation tips 76.Hive Data Warehouse report design, HWI, CLI client demonstrations, and development practices for user-defined functions (UDFs)
Third Day Afternoon	13, Mahout Big Data analysis mining platform	77. Big Data Analysis mining technology introduction, and industry Big Data Mining application case Architecture, core algorithm and key technology application of 78.Mahout Big Data mining platform 79. Mahout-based data mining application development combat Installation deployment and configuration optimization for 80.Mahout clusters 81. Integrated Mahout and Hadoop integrated Big Data Mining platform application combat
	14, Big Data Intelligent ETL operation and Hadoop cluster operation and maintenance monitoring tool platform Application	Framework for data conversion between 82.Hadoop and DBMS How 83.Sqoop import and export data works, as well as sqoop cluster installation deployment and configuration 84.Kettle cluster platform architecture, core technology working principle and application case 85.Kettle cluster installation deployment and configuration, and application development combat 86. Using Sqoop to implement data import and export interactions between MySQL and Hadoop clusters 87.Hadoop Big Data operation and maintenance monitoring System installation deployment and configuration optimization of Hue platform
	Application of Big Data project	88. Implementation of big Data complete project deployment design and application development practices according to practical application cases

Chengdu Big Data Hadoop and Spark technology training course