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Course Outline
Big Data Landscape:
- Definition and scope of Big Data
- Factors driving the growing popularity of Big Data
- Real-world Big Data Case Studies
- Key characteristics of Big Data
- Solution frameworks for managing Big Data
Hadoop and Its Core Components:
- Introduction to Hadoop and its primary components
- Hadoop architecture and the types of data it can handle and process
- Historical context of Hadoop, including adoption by various companies and their motivations
- Detailed explanation of the Hadoop framework and its components
- Understanding HDFS (Hadoop Distributed File System) and its read/write operations
- Setting up Hadoop clusters in various modes: Standalone, Pseudo-distributed, and Multi-node
(This section covers establishing a Hadoop cluster using VirtualBox, KVM, or VMware, configuring necessary network settings, launching Hadoop daemons, and validating cluster functionality).
- The MapReduce framework and its operational principles
- 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 cluster planning
- Analyzing workloads to prevent failures and optimize performance
Introduction to MapR and Its Advantages:
- Overview of MapR architecture
- Deep dive into MapR Control System, MapR Volumes, snapshots, and mirrors
- Cluster planning specific to MapR environments
- Comparative analysis of MapR against other distributions and Apache Hadoop
- MapR installation procedures and cluster deployment
Cluster Setup and Administration:
- Managing services, nodes, snapshots, mirrored volumes, and remote clusters
- Comprehending and managing nodes effectively
- Understanding Hadoop components and installing them alongside MapR services
- Accessing cluster data via NFS and managing associated services and nodes
- Data management using volumes, user and group management, node role assignment, node commissioning and decommissioning, cluster administration, performance monitoring, metric analysis for performance optimization, and MapR security configuration and administration
- Working with M7 native storage for MapR tables
- Configuring and tuning the cluster for optimal performance
Cluster Upgrades and Integration:
- Upgrading MapR software versions and understanding upgrade types
- Configuring the MapR cluster to interface with an HDFS cluster
- Deploying a MapR cluster on Amazon Elastic MapReduce
All topics include demonstrations and hands-on practice sessions to provide learners with practical experience.
Requirements
- Foundational knowledge of the Linux file system
- Basic Java proficiency
- Familiarity with Apache Hadoop (recommended)
28 Hours
Testimonials (1)
practical things of doing, also theory was served good by Ajay
