Apache Hadoop: Manipulation and Transformation of Data Performance Training Course

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Course CodeCourse Code

ApHadm1

Duration Duration

21 hours (usually 3 days including breaks)

Requirements 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 Overview


This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis.

The major focus of the course is data manipulation and transformation.

Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation.

This training also addresses performance metrics and performance optimisation.

The course is entirely hands on and is punctuated by presentations of the theoretical aspects.

Course OutlineCourse 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

Bookings, Prices and EnquiriesBookings, Prices and Enquiries

Private Classroom
 
Private Classroom
Participants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
Private Remote
Price not set yet
Private Remote
The instructor and the participants are in two different physical locations and communicate via the Internet. More Information

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Private Remote
1
2 0EUR
3 0EUR
4 0EUR
Public Classroom
Price not set yet
Request
Public Classroom
Participants from multiple organisations. Topics usually cannot be customised

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Public Classroom
1
2 50EUR
3 67EUR
4 75EUR
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