Corso di formazione Apache Hadoop: Manipulation and Transformation of Data Performance

  1. Big Data Training
  2. Hadoop Training
  3. Corso di formazione Apache Hadoop: Manipulation and Transformation of Data Performance

Codice del corso

ApHadm1

Durata

21 ore (generalmente 3 giorni pause incluse)

Requisiti

Attendees are not required to have any specific skill as the training is focused on end users skills for both the administration and the manipulation of data under Apache Hadoop

Overview


Questo corso è destinato a sviluppatori, architetti, data scientist o qualsiasi profilo che richieda l'accesso ai dati in modo intensivo o su base regolare.

L'obiettivo principale del corso è la manipolazione e la trasformazione dei dati.

Tra gli strumenti dell'ecosistema Hadoop questo corso include l'uso di Pig e Hive entrambi ampiamente utilizzati per la trasformazione e la manipolazione dei dati.

Questa formazione affronta anche le metriche e l'ottimizzazione delle prestazioni.

Il corso è interamente pratico ed è scandito da presentazioni degli aspetti teorici.

Machine Translated

Struttura del corso

1.1Hadoop Concepts

1.1.1HDFS

  • The Design of HDFS
  • Command line interface
  • Hadoop File System

1.1.2Clusters

  • Anatomy of a cluster
  • Mater Node / Slave node
  • Name Node / Data Node

1.2Data Manipulation

1.2.1MapReduce detailed

  • Map phase
  • Reduce phase
  • Shuffle

1.2.2Analytics with Map Reduce

  • Group-By with MapReduce
  • Frequency distributions and sorting with MapReduce
  • Plotting results (GNU Plot)
  • Histograms with MapReduce
  • Scatter plots with MapReduce
  • Parsing complex datasets
  • Counting with MapReduce and Combiners
  • Build reports

 

1.2.3Data Cleansing

  • Document Cleaning
  • Fuzzy string search
  • Record linkage / data deduplication
  • Transform and sort event dates
  • Validate source reliability
  • Trim Outliers

1.2.4Extracting and Transforming Data

  • Transforming logs
  • Using Apache Pig to filter
  • Using Apache Pig to sort
  • Using Apache Pig to sessionize

1.2.5Advanced Joins

  • Joining data in the Mapper using MapReduce
  • Joining data using Apache Pig replicated join
  • Joining sorted data using Apache Pig merge join
  • Joining skewed data using Apache Pig skewed join
  • Using a map-side join in Apache Hive
  • Using optimized full outer joins in Apache Hive
  • Joining data using an external key value store

1.3Performance Diagnosis and Optimization Techniques

  • Map
    • Investigating spikes in input data
    • Identifying map-side data skew problems
    • Map task throughput
    • Small files
    • Unsplittable files
  • Reduce
    • Too few or too many reducers
    • Reduce-side data skew problems
    • Reduce tasks throughput
    • Slow shuffle and sort
  • Competing jobs and scheduler throttling
  • Stack dumps & unoptimized code
  • Hardware failures
  • CPU contention
  • Tasks
    • Extracting and visualizing task execution times
    • Profiling your map and reduce tasks
  • Avoid the reducer
  • Filter and project
  • Using the combiner
  • Fast sorting with comparators
  • Collecting skewed data
  • Reduce skew mitigation

Recensioni

★★★★★
★★★★★

Categorie relative

Corsi relativi

Alluxio: Unifying Disparate Storage SystemsAdministrator Training for Apache HadoopDatameer for Data AnalystsHadoop AdministrationHadoop For AdministratorsHadoop for Business AnalystsHadoop for Developers (4 days)Advanced Hadoop for DevelopersHadoop for Developers and AdministratorsHadoop for Project ManagersHadoop Administration on MapRHBase for DevelopersApache Avro: Data Serialization for Distributed ApplicationsSamza for Stream ProcessingTigon: Real-time Streaming for the Real World

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