Course Code

ApHadm1

Duration

21 hours (usually 3 days including breaks)

Requirements

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

Deze cursus is bedoeld voor ontwikkelaars, architecten, datawetenschappers of elk profiel dat intensieve of regelmatige toegang tot gegevens vereist.

De belangrijkste focus van de cursus ligt op gegevensmanipulatie en transformatie.

Onder de tools in het Hadoop ecosysteem omvat deze cursus het gebruik van Pig en Hive beide veel worden gebruikt voor gegevenstransformatie en -manipulatie.

Deze training behandelt ook prestatiestatistieken en prestatie-optimalisatie.

De cursus is volledig praktisch en wordt onderbroken door presentaties van de theoretische aspecten.

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Course Outline

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

Getuigenissen

★★★★★

Wat vond je het leukst aan de training ?: Gedetailleerd op elke tool.

Amber Mehrotra - NIIT Limited

Cursus:Hadoop Administration

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Het feit dat alle gegevens en software klaar waren om te gebruiken op een reeds voorbereide VM, geleverd door de trainer op externe schijven.

vyzVoice

Cursus:Hadoop for Developers and Administrators

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Ik vond vooral de trainer die echte voorbeelden gaf.

Simon Hahn