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Course Outline
Big Data Overview:
- Defining Big Data
- Reasons for the growing popularity of Big Data
- Real-world Big Data Case Studies
- Key Characteristics of Big Data
- Solutions for processing Big Data
Hadoop and Its Components:
- Introduction to Hadoop and its core components
- Hadoop Architecture and the types of data it can handle and process
- A brief history of Hadoop, key companies adopting it, and their motivations
- Detailed explanation of the Hadoop Framework and its components
- Understanding HDFS and the operations of reading from and writing to the Hadoop Distributed File System
- Steps to set up a Hadoop Cluster in various modes: Standalone, Pseudo-distributed, and Multi-node
(This section covers establishing a Hadoop cluster using VirtualBox, KVM, or VMware, configuring the necessary network settings, starting Hadoop Daemons, and testing cluster functionality).
- Explanation of the MapReduce framework and its operational mechanics
- Executing MapReduce jobs on a Hadoop cluster
- Concepts of replication, mirroring, and rack awareness within Hadoop clusters
Hadoop Cluster Planning:
- Strategies for planning your Hadoop cluster
- Evaluating hardware and software requirements for cluster planning
- Analyzing workloads to prevent failures and optimize cluster performance
Introduction to MapR and Its Value:
- Overview of MapR and its architecture
- Exploring and working with MapR Control System, MapR Volumes, snapshots, and mirrors
- Planning a cluster specifically for the MapR environment
- Comparing MapR with other distributions and Apache Hadoop
- Installation and deployment of MapR clusters
Cluster Setup and Administration:
- Managing services, nodes, snapshots, mirrored volumes, and remote clusters
- Understanding and managing cluster nodes
- Gaining insight into Hadoop components and installing them alongside MapR services
- Accessing data on the cluster, including via NFS, while managing services and nodes
- Data management using volumes, user and group management, role assignment to nodes, node commissioning and decommissioning, cluster administration, performance monitoring, metric configuration and analysis, and MapR security administration
- Understanding and utilizing M7 Native storage for MapR tables
- Configuring and tuning the cluster for optimum performance
Cluster Upgrades and Integration:
- Upgrading MapR software versions and understanding upgrade types
- Configuring the MapR cluster to access an HDFS cluster
- Setting up a MapR cluster on Amazon Elastic Mapreduce
All the above topics include demonstrations and practice sessions to provide learners with hands-on experience of the technology.
Requirements
- Foundational knowledge of the Linux File System
- Basic understanding of Java
- Familiarity with Apache Hadoop (recommended)
28 Hours
Testimonials (1)
practical things of doing, also theory was served good by Ajay
