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Course Outline
Overview of Big Data:
- Defining Big Data
- Reasons behind the rising popularity of Big Data
- Case studies involving Big Data
- Key characteristics of Big Data
- Solutions for managing Big Data
Hadoop and Its Components:
- Definition of Hadoop and its core components
- Hadoop architecture and its capability to handle specific data types
- A brief history of Hadoop, including the companies that use it and the reasons for adoption
- Detailed explanation of the Hadoop framework and its components
- Explanation of HDFS and the processes for reading from and writing to the Hadoop Distributed File System
- Procedures for setting 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, addressing critical network configurations, starting Hadoop Daemons, and performing cluster testing).
- Explanation of the Map Reduce framework and its operational mechanics
- Executing Map Reduce jobs on a Hadoop cluster
- Comprehending replication, mirroring, and rack awareness within Hadoop clusters
Planning a Hadoop Cluster:
- Strategies for planning your Hadoop cluster
- Evaluating hardware and software requirements for cluster planning
- Analyzing workloads to plan the cluster effectively, preventing failures, and ensuring optimal performance
Introduction to MapR and Its Value:
- An overview of MapR and its architecture
- Understanding and utilizing the MapR Control System, MapR Volumes, snapshots, and mirrors
- Planning clusters specifically for MapR
- Comparing MapR with other distributions and Apache Hadoop
- Process of MapR installation and cluster deployment
Cluster Setup and Administration:
- Managing services, nodes, snapshots, mirror volumes, and remote clusters
- Understanding and managing 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, cluster administration, performance monitoring, configuring and analyzing metrics, and administering MapR security
- Understanding and working with M7, the native storage for MapR tables
- Configuring and tuning the cluster for optimum performance
Cluster Upgrades and Integration with Other Systems:
- Upgrading the MapR software version and types of upgrades
- Configuring the MapR cluster to access an HDFS cluster
- Setting up a MapR cluster on Amazon Elastic MapReduce
All topics include demonstrations and practice sessions to provide learners with hands-on experience.
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
