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
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 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
Duration
35 hours (usually 5 days including breaks)
Requirements
- Basic Linux administration skills
- Basic programming skills
Overview
Audience:
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
Course Outline
1: HDFS (17%)
- Describe the function of HDFS Daemons
- Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
- Identify current features of computing systems that motivate a system like Apache Hadoop.
- Classify major goals of HDFS Design
- Given a scenario, identify appropriate use case for HDFS Federation
- Identify components and daemon of an HDFS HA-Quorum cluster
- Analyze the role of HDFS security (Kerberos)
- Determine the best data serialization choice for a given scenario
- Describe file read and write paths
- Identify the commands to manipulate files in the Hadoop File System Shell
2: YARN and MapReduce version 2 (MRv2) (17%)
- Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings
- Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
- Understand basic design strategy for MapReduce v2 (MRv2)
- Determine how YARN handles resource allocations
- Identify the workflow of MapReduce job running on YARN
- Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN.
3: Hadoop Cluster Planning (16%)
- Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
- Analyze the choices in selecting an OS
- Understand kernel tuning and disk swapping
- Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
- Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
- Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
- Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
- Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
4: Hadoop Cluster Installation and Administration (25%)
- Given a scenario, identify how the cluster will handle disk and machine failures
- Analyze a logging configuration and logging configuration file format
- Understand the basics of Hadoop metrics and cluster health monitoring
- Identify the function and purpose of available tools for cluster monitoring
- Be able to install all the ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig
- Identify the function and purpose of available tools for managing the Apache Hadoop file system
5: Resource Management (10%)
- Understand the overall design goals of each of Hadoop schedulers
- Given a scenario, determine how the FIFO Scheduler allocates cluster resources
- Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
- Given a scenario, determine how the Capacity Scheduler allocates cluster resources
6: Monitoring and Logging (15%)
- Understand the functions and features of Hadoop’s metric collection abilities
- Analyze the NameNode and JobTracker Web UIs
- Understand how to monitor cluster Daemons
- Identify and monitor CPU usage on master nodes
- Describe how to monitor swap and memory allocation on all nodes
- Identify how to view and manage Hadoop’s log files
- Interpret a log file
