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Course Outline
Big Data Overview:
- Defining Big Data
- The drivers behind the growing popularity of Big Data
- Real-world Big Data Case Studies
- Key Characteristics of Big Data
- Available solutions for managing Big Data.
Hadoop and Its Components:
- Introduction to Hadoop and its core components.
- Hadoop Architecture and its capabilities regarding data processing and handling.
- A brief history of Hadoop, including companies that have adopted it and the reasons for doing so.
- Detailed explanation of the Hadoop framework and its components.
- Understanding HDFS and the mechanics of reading and writing to the Hadoop Distributed File System.
- Instructions for setting up a Hadoop cluster in various modes: Stand-alone, Pseudo-distributed, and Multi-Node.
(This section covers setting up a Hadoop cluster using VirtualBox, KVM, or VMware, essential network configurations, launching Hadoop Daemons, and testing the cluster).
- Introduction to the Map Reduce framework and its operational principles.
- Executing Map Reduce jobs on a Hadoop cluster.
- Exploring Replication, Mirroring, and Rack Awareness within Hadoop clusters.
Planning a Hadoop Cluster:
- Strategies for planning your Hadoop cluster.
- Aligning hardware and software requirements for cluster planning.
- Analyzing workloads and planning the cluster to prevent failures and ensure optimal performance.
Introduction to MapR and Its Advantages:
- An overview of MapR and its architecture.
- Understanding and utilizing 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 processes.
Cluster Setup and Administration:
- Managing services, nodes, snapshots, mirrored 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.
- Data management using volumes, user and group management, assigning roles to nodes, node commissioning and decommissioning, cluster administration, performance monitoring, configuring and analyzing metrics, and administering MapR security.
- Understanding and working with M7-native storage for MapR tables.
- Cluster configuration and tuning for optimal performance.
Cluster Upgrades and Integration with Other Setups:
- Upgrading MapR software versions and understanding upgrade types.
- Configuring the MapR cluster to access an HDFS cluster.
- Deploying a MapR cluster on Amazon Elastic Mapreduce.
All topics above include demonstrations and practice sessions to provide learners with hands-on experience with the technology.
Requirements
- Foundational knowledge of Linux File Systems
- Basic understanding of Java
- Familiarity with Apache Hadoop (recommended)
28 Hours
Testimonials (1)
practical things of doing, also theory was served good by Ajay
