TRAINING HADOOP DEVELOPER WITH SPARK

HADOOP DEVELOPER WITH SPARK

www.purnamaacademy.com , Hotline (Call/SMS/WA) :  0838-0838-0001 , Jakarta , Bandung, Bali

Syllabus Overview

Saat ini Industri banyak menggunakan Hadoop secara ekstensif untuk menganalisis kumpulan data yang mereka miliki ,  alasannya adalah bahwa framework Hadoop bekerja atas dasar pada model pemrograman sederhana (MapReduce) dan memungkinkan solusi komputasi yang terukur, fleksibel, toleransi kesalahan dan hemat biaya. Disini, yang menjadi perhatian utama adalah menjaga kecepatan dalam mengolah dataset besar dalam hal waktu tunggu antara Query  dan waktu tunggu untuk menjalankan program.

Spark diperkenalkan oleh Apache Software Foundation untuk mempercepat proses komputasi komputasi Hadoop

Tidak seperti yang kebanyakan orang kira bahwa Spark bukanlah versi modifikasi dari Hadoop dan sebenarnya juga tidak bergantung pada Hadoop karena memiliki manajemen cluster sendiri. Hadoop hanyalah salah bagian dalam implementasi Spark.

Spark menggunakan Hadoop dengan dua cara – Pertama adalah penyimpanan dan yang kedua adalah pemrosesan. Namun karena Spark memiliki perhitungan manajemen cluster sendiri maka Spark menggunakan Hadoop lebih kepada untuk tujuan penyimpanan saja.

Class Type           : Training

Duration              : 4 Days (09.00 – 17.00)

Venue / Price    : Click Here ,

Registration       : Click Here 

Topics include:

Introduction to Hadoop and the Hadoop Ecosystem

·         Problems with Traditional Large-scale Systems

·         Hadoop!

·         The Hadoop EcoSystem

Hadoop Architecture and HDFS

·         Distributed Processing on a Cluster

·         Storage: HDFS Architecture

·         Storage: Using HDFS

·         Resource Management: YARN Architecture

·         Resource Management: Working with YARN

Importing Relational Data with Apache Sqoop

·         Sqoop Overview

·         Basic Imports and Exports

·         Limiting Results

·         Improving Sqoop’s Performance

·         Sqoop 2

Introduction to Impala and Hive

·         Introduction to Impala and Hive

·         Why Use Impala and Hive?

·         Comparing Hive to Traditional Databases

·         Hive Use Cases

Modeling and Managing Data with Impala and Hive

·         Data Storage Overview

·         Creating Databases and Tables

·         Loading Data into Tables

·         HCatalog

·         Impala Metadata Caching

Data Formats

·         Selecting a File Format

·         Hadoop Tool Support for File Formats

·         Avro Schemas

·         Using Avro with Hive and Sqoop

·         Avro Schema Evolution

·         Compression

Data Partitioning

·         Partitioning Overview

·         Partitioning in Impala and Hive

Capturing Data with Apache Flume

·         What is Apache Flume?

·         Basic Flume Architecture

·         Flume Sources

·         Flume Sinks

·         Flume Channels

·         Flume Configuration

Spark Basics

·         What is Apache Spark?

·         Using the Spark Shell

·         RDDs (Resilient Distributed Datasets)

·         Functional Programming in Spark

Working with RDDs in Spark

·         A Closer Look at RDDs

·         Key-Value Pair RDDs

·         MapReduce

·         Other Pair RDD Operations

Writing and Deploying Spark Applications

·         Spark Applications vs. Spark Shell

·         Creating the SparkContext

·         Building a Spark Application (Scala and Java)

·         Running a Spark Application

·         The Spark Application Web UI

·         Configuring Spark Properties

·         Logging

Parallel Programming with Spark

·         Review: Spark on a Cluster

·         RDD Partitions

·         Partitioning of File-based RDDs

·         HDFS and Data Locality

·         Executing Parallel Operations

·         Stages and Tasks

Spark Caching and Persistence

·         RDD Lineage

·         Caching Overview

·         Distributed Persistence

Common Patterns in Spark Data Processing

·         Common Spark Use Cases

·         Iterative Algorithms in Spark

·         Graph Processing and Analysis

·         Machine Learning

·         Example: k-means

Preview: Spark SQL

·         Spark SQL and the SQL Context

·         Creating DataFrames

·         Transforming and Querying DataFrames

·         Saving DataFrames

·         Comparing Spark SQL with Impala

Participants :  (Hadoop Developer , Big Data Analyst, IT Developer, DBA  )

Speaker  : Purnama Academy Trainer

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Categories: big data, hadoop developer, hadoop training, pelatihan, seminar, spark hadoop, training, training bigdata