Duration
7 hours (usually 1 day including breaks)
Requirements
- An understanding of Big Data concepts
- An understanding of Hadoop concepts
- Java programming experience
- Comfortable using a Linux command line
Overview
In this instructor-led, live training, participants will learn the core concepts behind MapR Stream Architecture as they develop a real-time streaming application.
By the end of this training, participants will be able to build producer and consumer applications for real-time stream data procesing.
Audience
- Developers
- Administrators
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
Overview of MapR Streams Architecture
MapR Stream Core Components
Understanding How Messages Are Managed in MapR Streams
Understanding Producers and Consumers
Developing a MapR Streams Application
- Streams, Producer, Consumer
- Using the Kafka Java API
Working with Properties and Options
Summary and Conclusion
Duration
28 hours (usually 4 days including breaks)
Requirements
- Basic knowledge of Linux FS
- Basic Java
- Knowledge of Apache Hadoop (recommended)
Overview
Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
Course Outline
Big Data Overview:
- What is Big Data
- Why Big Data is gaining popularity
- Big Data Case Studies
- Big Data Characteristics
- Solutions to work on Big Data.
Hadoop & Its components:
- What is Hadoop and what are its components.
- Hadoop Architecture and its characteristics of Data it can handle /Process.
- Brief on Hadoop History, companies using it and why they have started using it.
- Hadoop Frame work & its components- explained in detail.
- What is HDFS and Reads -Writes to Hadoop Distributed File System.
- How to Setup Hadoop Cluster in different modes- Stand- alone/Pseudo/Multi Node cluster.
(This includes setting up a Hadoop cluster in VirtualBox/KVM/VMware, Network configurations that need to be carefully looked into, running Hadoop Daemons and testing the cluster).
- What is Map Reduce frame work and how it works.
- Running Map Reduce jobs on Hadoop cluster.
- Understanding Replication , Mirroring and Rack awareness in context of Hadoop clusters.
Hadoop Cluster Planning:
- How to plan your hadoop cluster.
- Understanding hardware-software to plan your hadoop cluster.
- Understanding workloads and planning cluster to avoid failures and perform optimum.
What is MapR and why MapR :
- Overview of MapR and its architecture.
- Understanding & working of MapR Control System, MapR Volumes , snapshots & Mirrors.
- Planning a cluster in context of MapR.
- Comparison of MapR with other distributions and Apache Hadoop.
- MapR installation and cluster deployment.
Cluster Setup & Administration:
- Managing services, nodes ,snapshots, mirror volumes and remote clusters.
- Understanding and managing Nodes.
- Understanding of Hadoop components, Installing Hadoop components alongside MapR Services.
- Accessing Data on cluster including via NFS Managing services & nodes.
- Managing data by using volumes, managing users and groups, managing & assigning roles to nodes, commissioning decommissioning of nodes, cluster administration and performance monitoring, configuring/ analyzing and monitoring metrics to monitor performance, configuring and administering MapR security.
- Understanding and working with M7- Native storage for MapR tables.
- Cluster configuration and tuning for optimum performance.
Cluster upgrade and integration with other setups:
- Upgrading software version of MapR and types of upgrade.
- Configuring Mapr cluster to access HDFS cluster.
- Setting up MapR cluster on Amazon Elastic Mapreduce.
All the above topics include Demonstrations and practice sessions for learners to have hands on experience of the technology.
