Apache Hadoop for Administrators

Apache Hadoop for Administrators

Apache Hadoop is an open source, scalable, massively parallel, in-memory database environment for data farms and data lakes. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

SKU: BD-0008 Category: Big Data Tag: Big Data

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Description

This course prepares anyone for the Apache Hadoop platform. There are two primary distributions for Hadoop, that of Hortonworks and Cloudera. This course is architected in a way that allows maximum customization for your needs.

You can decide if the students need Hortonworks, Cloudera, or both. This affects labs and has no effect on course length.

The Administrator course deals with 3-5 deployed servers. Now the course may use Docker if you wish to deploy these servers.

Why We Learn Hadoop:

To have an increase in their access to Big Data, to make use of existing Big Data investments, pace up with growing Enterprise Adoption and because over all, there is has recently been an increase in demand for Hadoop Developers.

Course Audience:

Administrators who wish to explore Data Science and Big Data

Course Duration: 3 days

BIG DATA

What is Big Data?
Typical Distributed Systems
A Short History of Hadoop
Who are the players?
Hadoop Alternatives

HADOOP

What is Hadoop
YARN
Key differences between 1.X and 2.X

HDFS

What is HDFS
HDFS Architecture
Writing and reading files
Understanding Block storage
Nodes
HDFS client connections

YARN

MapReduce as a Pattern
MapReduce YARN Style
Tracing MapReduce Job on Hadoop 2.0

HADOOP CLUSTER

Most common cluster topology
Sizing considerations
Hardware, Software, OS, network considerations
File Systems – Windows, and Linux
Hadoop Configuration Files

ADVANCED CLUSTERING

Advanced HDFS
Advanced MapReduce
Advanced YARN configurations
Rack Aware Clusters
Including and Excluding Hosts

CLUSTER MONITORING & MANAGEMENT

Monitoring Options in the Ecosystem
Monitoring YARN-based clusters
Ganglia on Hadoop
Nagios
Interop of Ganglia & Nagios
Other options
Monitoring Java Processes
Understanding JVM Memory
Memory Analyzers
MX Monitoring

HADOOP SERVICES

HDFS NFS Gateway
Configuring NFS Gateway
Using NFS Gateway Services

HADOOP SECURITY

Security over Hadoop
Kerberos
Authentication & Authorization
Knox

CLUSTER MAINTENANCE

HDFS snapshots
Backing up HDFS Clusters
Commissioning Hadoop nodes
Load Balancing Hadoop

SCHEDULERS

Hadoop Job Scheduling
Built-in schedulers
Capacity schedulers and Queues
Configuring Schedulers & Permissions

HIVE & PIG

Introduction to Hive & Pig
Comparing Hive with RDBMS
Hive & Pig Components & Metastore
HiveServer2
Hive Command Line Interface
Defining Hive Tables
Loading Data into Hive & Performing Queries
Hive Security
Pig Tables & Syntax

OOZIE

Introduction to Oozie
Architecture
Administration of Oozie

SQOOP

The Sqoop Import Tool
Importing & Exporting data with Sqoop

FLUME

Flume Sources, Sinks and Channels
Flume Interceptors
Flume configuration
Monitoring Flume

MOVING DATA

Data Movement with Hadoop
ETL using Hadoop
Ingesting data with Hadoop
Using Hue
Distributed Copy

HADOOP HA

HA for Hadoop
How HA works
Failover on Hadoop

Slide demo:

MapReduce-Patterns

Additional information

Course Duration	3 Day
Location	On-site, Remotely
Lab Count	18 Labs

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