Formation Hadoop pour Développeurs, niveau Acvancé

Code formation

hadoopdeva

Durée

21 heures (généralement 3 jours pauses comprises)

Pré requis

  • comfortable with Java programming language (most programming exercises are in java)
  • comfortable in Linux environment (be able to navigate Linux command line, edit files using vi / nano)
  • a working  knowledge of Hadoop.

Lab environment

Zero Install: There is no need to install hadoop software on students’ machines! A working hadoop cluster will be provided for students.

Students will need the following

  • an SSH client (Linux and Mac already have ssh clients, for Windows Putty is recommended)
  • a browser to access the cluster. We recommend Firefox browser

Aperçu

Apache Hadoop est l’un des frameworks les plus populaires pour le traitement du Big Data sur des clusters de serveurs. Ce cours aborde la gestion des données dans HDFS, Pig, Hive et HBase. Ces techniques de programmation avancées seront utiles aux développeurs expérimentés Hadoop .

Public : développeurs

Durée: trois jours

Format: cours magistraux (50%) et travaux pratiques (50%).

Machine Translated

Plan du cours

Section 1: Data Management in HDFS

  • Various Data Formats (JSON / Avro / Parquet)
  • Compression Schemes
  • Data Masking
  • Labs : Analyzing different data formats;  enabling compression

Section 2: Advanced Pig

  • User-defined Functions
  • Introduction to Pig Libraries (ElephantBird / Data-Fu)
  • Loading Complex Structured Data using Pig
  • Pig Tuning
  • Labs : advanced pig scripting, parsing complex data types

Section 3 : Advanced Hive

  • User-defined Functions
  • Compressed Tables
  • Hive Performance Tuning
  • Labs : creating compressed tables, evaluating table formats and configuration

Section 4 : Advanced HBase

  • Advanced Schema Modelling
  • Compression
  • Bulk Data Ingest
  • Wide-table / Tall-table comparison
  • HBase and Pig
  • HBase and Hive
  • HBase Performance Tuning
  • Labs : tuning HBase; accessing HBase data from Pig & Hive; Using Phoenix for data modeling

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