Spark for Developers 교육 과정

  1. 빅 데이터 Training
  2. Apache Spark Training
  3. Spark for Developers 교육 과정

Course Code

sparkdev

Duration

21 hours (usually 3 days including breaks)

Requirements

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)

Overview

목표:

이 과정은 Apache Spark 를 소개합니다. 학생들은 Spark가 Big Data 생태계에 어떻게 적용되는지, 그리고 데이터 분석에 Spark를 사용하는 방법을 배우게됩니다. 이 과정은 대화식 데이터 분석, Spark 내부, Spark API, Spark SQL , Spark 스트리밍, 기계 학습 및 graphX를위한 Spark 셸을 다룹니다.

오디언스 :

개발자 / 데이터 분석가

Machine Translated

Course Outline

  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

회원 평가

★★★★★
★★★★★

Related Categories

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