Apache Hadoop: Manipulation and Transformation of Data Performance培训

课程编码

ApHadm1

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

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

课程概览


本课程面向开发人员,架构师,数据科学家或任何需要密集或定期访问数据的配置文件。

该课程的主要重点是数据处理和转换。

Hadoop生态系统的工具中,本课程包括Pig和Hive的使用,这两者都大量用于数据转换和操作。

此培训还涉及性能指标和性能优化。

该课程完全是实践性的,并通过理论方面的介绍来打断。

Machine Translated

课程大纲

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

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