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Course Outline
Big Data Overview:
- Definition of Big Data
- Reasons behind the growing popularity of Big Data
- Case studies on Big Data
- Key characteristics of Big Data
- Solutions for managing Big Data
Hadoop and Its Components:
- Understanding Hadoop and its core components
- Hadoop architecture and the characteristics of data it can handle and process
- A brief history of Hadoop, including companies that use it and the motivations behind their adoption
- Detailed explanation of the Hadoop framework and its components
- Explanation of HDFS and the read/write operations within the Hadoop Distributed File System
- Procedures for setting up a Hadoop cluster in various modes: standalone, pseudo-distributed, and multi-node cluster
(This section covers setting up a Hadoop cluster on VirtualBox, KVM, or VMware, addressing critical network configurations, running Hadoop daemons, and testing the cluster).
- Overview of the MapReduce framework and its operational mechanisms
- 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
- Aligning hardware and software requirements for effective cluster planning
- Analyzing workloads to plan a cluster that prevents failures and ensures optimal performance
Introduction to MapR and Its Advantages:
- Overview of MapR and its architecture
- Understanding and working with the MapR Control System, MapR Volumes, snapshots, and mirrors
- Planning a cluster specifically for MapR environments
- Comparing MapR with other distributions and Apache Hadoop
- MapR installation and cluster deployment processes
Cluster Setup and Administration:
- Managing services, nodes, snapshots, mirrored volumes, and remote clusters
- Understanding and managing nodes
- Understanding Hadoop components and installing them alongside MapR services
- Accessing cluster data, including via NFS, and managing services and nodes
- Managing data through volumes, user and group management, role assignment to nodes, node commissioning and decommissioning, cluster administration, performance monitoring, configuring and analyzing performance metrics, and administering MapR security
- Understanding and working with M7 native storage for MapR tables
- Configuring and tuning the cluster for optimal performance
Cluster Upgrade and Integration with Other Setups:
- 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 with the technology.
Requirements
- Fundamental knowledge of Linux file systems
- Basic Java programming skills
- Familiarity with Apache Hadoop (recommended)
28 Hours
Testimonials (1)
practical things of doing, also theory was served good by Ajay
