Last updated
Kurskod
ApHadm1
Varaktighet
21 timmar (vanligtvis 3 dag inklusive pauser)
Krav
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
Översikt
Kursen är avsedd för utvecklare, arkitekter, datavetenskapare eller någon profil som kräver tillgång till data antingen intensivt eller regelbundet Kursens huvudfokus är dataprofilering och transformation Bland de verktyg som ingår i Hadoop ekosystemet ingår i denna kurs användningen av gris och hiva som båda används kraftigt för datatransformation och manipulation Denna utbildning behandlar även prestandametri och prestationsoptimering Kursen handlar helt och hållet och skiljer sig genom presentationer av de teoretiska aspekterna .
Kursplan
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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