 班级规模及环境--热线:4008699035 手机:15921673576( 微信同号) |
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坚持小班授课，为保证培训效果，增加互动环节，每期人数限3到5人。 |
| 上课时间和地点 |
开课地址：【上海】同济大学(沪西)/新城金郡商务楼(11号线白银路站)【深圳分部】：电影大厦(地铁一号线大剧院站) 【武汉分部】：佳源大厦【成都分部】：领馆区1号【沈阳分部】：沈阳理工大学【郑州分部】：锦华大厦【石家庄分部】：瑞景大厦【北京分部】：北京中山 【南京分部】：金港大厦
新开班 (连续班 、周末班、晚班）：2026年1月26日..课程再次升级....学用相长,注重实践....以质量求发展....合作共赢....实用实战....实战培训....用心服务..........--即将开课--............................(欢迎您垂询，视教育质量为生命！) |
| 实验设备 |
☆资深工程师授课
☆注重质量
☆边讲边练
☆合格学员免费推荐工作
★实验设备请点击这儿查看★ |
| 质量保障 |
1、培训过程中，如有部分内容理解不透或消化不好，可免费在以后培训班中重听；
2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供半年的技术支持。
3、培训合格学员可享受免费推荐就业机会。 |
课程大纲 |
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Scala primer
A quick introduction to Scala
Labs : Getting know Scala
Spark Basics
Background and history
Spark and Hadoop
Spark concepts and architecture
Spark eco system (core, spark sql, mlib, streaming)
Labs : Installing and running Spark
First Look at Spark
Running Spark in local mode
Spark web UI
Spark shell
Analyzing dataset – part 1
Inspecting RDDs
Labs: Spark shell exploration
RDDs
RDDs concepts
Partitions
RDD Operations / transformations
RDD types
Key-Value pair RDDs
MapReduce on RDD
Caching and persistence
Labs : creating & inspecting RDDs; Caching RDDs
Spark API programming
Introduction to Spark API / RDD API
Submitting the first program to Spark
Debugging / logging
Configuration properties
Labs : Programming in Spark API, Submitting jobs
Spark SQL
SQL support in Spark
Dataframes
Defining tables and importing datasets
Querying data frames using SQL
Storage formats : JSON / Parquet
Labs : Creating and querying data frames; evaluating data formats
MLlib
MLlib intro
MLlib algorithms
Labs : Writing MLib applications
GraphX
GraphX library overview
GraphX APIs
Labs : Processing graph data using Spark
Spark Streaming
Streaming overview
Evaluating Streaming platforms
Streaming operations
Sliding window operations
Labs : Writing spark streaming applications
Spark and Hadoop
Hadoop Intro (HDFS / YARN)
Hadoop + Spark architecture
Running Spark on Hadoop YARN
Processing HDFS files using Spark
Spark Performance and Tuning
Broadcast variables
Accumulators
Memory management & caching
Spark Operations
Deploying Spark in production
Sample deployment templates
Configurations
Monitoring
Troubleshooting
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