Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Big Data Overview
- Defining Big Data
- The drivers behind Big Data's growing popularity
- Big Data Case Studies
- Key Characteristics of Big Data
- Solutions for working with Big Data
Hadoop and Its Components
- Introduction to Hadoop and its core components
- Hadoop architecture and the types of data it can handle or process
- A brief history of Hadoop, the companies using it, and their motivations
- Detailed explanation of the Hadoop framework and its components
- Understanding HDFS and operations for reading and writing to the Hadoop Distributed File System
- Configuring a Hadoop cluster in various modes: standalone, pseudo-distributed, or multi-node
This section covers setting up a Hadoop cluster using VirtualBox, KVM, or VMware, including necessary network configurations, running Hadoop daemons, and testing the cluster.
- Understanding the MapReduce framework and its operational mechanics
- Executing MapReduce jobs on a Hadoop cluster
- Exploring replication, mirroring, and rack awareness within the context of Hadoop clusters
Planning a Hadoop Cluster
- Strategies for planning your Hadoop cluster
- Aligning hardware and software requirements for cluster planning
- Analyzing workloads to prevent failures and ensure optimal performance
Introduction to MapR and Why Choose MapR
- Overview of MapR and its architecture
- Understanding and utilizing the MapR Control System, MapR Volumes, snapshots, and mirrors
- Cluster planning specific to MapR environments
- Comparing MapR with other distributions and Apache Hadoop
- MapR installation and cluster deployment procedures
Cluster Setup and Administration
- Managing services, nodes, snapshots, mirrored volumes, and remote clusters
- Understanding and managing cluster nodes
- Comprehending Hadoop components and installing them alongside MapR services
- Accessing data on the cluster, including via NFS, and managing services and nodes
- Managing data through volumes, handling users and groups, assigning roles to nodes, commissioning and decommissioning nodes, overseeing cluster administration, monitoring performance, configuring and analyzing metrics, and administering MapR security
- Working with M7-native storage for MapR tables
- Configuring and tuning the cluster for optimal performance
Cluster Upgrades and Integration with Other Environments
- Upgrading MapR software versions and understanding upgrade types
- Configuring MapR clusters to access HDFS clusters
- Deploying MapR clusters on Amazon Elastic MapReduce
All topics covered include demonstrations and hands-on practice sessions to provide learners with practical experience of the technology.
Requirements
- Fundamental knowledge of the Linux File System
- Basic proficiency in Java
- Familiarity with Apache Hadoop (recommended)
28 Hours
Testimonials (1)
practical things of doing, also theory was served good by Ajay
