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Course Outline
Big Data Overview:
- Defining Big Data
- The reasons behind the rising popularity of Big Data
- Case studies in Big Data
- Key characteristics of Big Data
- Solutions for managing Big Data
Hadoop and Its Components:
- An introduction to Hadoop and its core components
- Hadoop architecture and the characteristics of data it can handle and process
- A brief history of Hadoop, companies adopting it, and the motivations behind their adoption
- Detailed explanation of the Hadoop framework and its components
- Understanding HDFS: reading from and writing to the Hadoop Distributed File System
- Setting up a Hadoop cluster in various modes: standalone, pseudo-distributed, and multi-node
(This section covers setting up a Hadoop cluster using VirtualBox, KVM, or VMware, addressing necessary network configurations, running Hadoop daemons, and testing the cluster).
- Overview of the MapReduce framework and its operational mechanics
- Executing MapReduce jobs on a Hadoop cluster
- Understanding 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 plan a cluster that prevents failures and ensures optimal performance
What is MapR and Why Choose MapR:
- Overview of MapR and its architecture
- Understanding and working with MapR Control System, MapR Volumes, snapshots, and mirrors
- Planning a cluster specifically for MapR
- Comparing MapR with other distributions and Apache Hadoop
- MapR installation and cluster deployment
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 data on the cluster, including via NFS, and managing services and nodes
- Managing data using 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, native storage for MapR tables
- Cluster configuration and tuning for optimum performance
Cluster Upgrade and Integration with Other Setups:
- Upgrading MapR software versions and types of upgrades
- Configuring the MapR cluster to access HDFS clusters
- 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
- Basic knowledge of Linux file systems
- Fundamental Java programming
- Knowledge of Apache Hadoop (recommended)
28 Hours
Testimonials (1)
practical things of doing, also theory was served good by Ajay
