Spark for Developers培训

  1. 大数据培训
  2. Apache Spark培训
  3. Spark for Developers培训

课程编码

sparkdev

课程时长

21 小时 通常来说是3天，包括中间休息。

要求

PRE-REQUISITES

familiarity with either Java / Scala / Python language (our labs in Scala and Python)
basic understanding of Linux development environment (command line navigation / editing files using VI or nano)

课程概览

目的：

本课程将介绍Apache Spark 。学生将学习Spark如何适应Big Data生态系统，以及如何使用Spark进行数据分析。该课程涵盖Spark shell，用于交互式数据分析，Spark内部，Spark API，Spark SQL ，Spark流，机器学习和graphX。

听众：

开发人员/数据分析师

Machine Translated

课程大纲

  1. Scala primer

    • A quick introduction to Scala
    • Labs : Getting know Scala

  2. 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

  3. First Look at Spark

    • Running Spark in local mode
    • Spark web UI
    • Spark shell
    • Analyzing dataset – part 1
    • Inspecting RDDs
    • Labs: Spark shell exploration

  4. 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

  5. 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

  6. 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

  7. MLlib

    • MLlib intro
    • MLlib algorithms
    • Labs : Writing MLib applications

  8. GraphX

    • GraphX library overview
    • GraphX APIs
    • Labs : Processing graph data using Spark

  9. Spark Streaming

    • Streaming overview
    • Evaluating Streaming platforms
    • Streaming operations
    • Sliding window operations
    • Labs : Writing spark streaming applications

  10. Spark and Hadoop

    • Hadoop Intro (HDFS / YARN)
    • Hadoop + Spark architecture
    • Running Spark on Hadoop YARN
    • Processing HDFS files using Spark

  11. Spark Performance and Tuning

    • Broadcast variables
    • Accumulators
    • Memory management & caching

  12. Spark Operations

    • Deploying Spark in production
    • Sample deployment templates
    • Configurations
    • Monitoring
    • Troubleshooting

客户评论

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课程分类

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