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Course Outline
Big Data Overview:
- Defining Big Data
- The reasons behind the growing popularity of Big Data
- Real-world Big Data Case Studies
- Key Characteristics of Big Data
- Solutions for managing Big Data.
Hadoop & Its Components:
- An introduction to Hadoop and its core components.
- Hadoop Architecture and the types of data it can handle or process.
- A brief history of Hadoop, the companies utilizing it, and the motivations behind its adoption.
- A detailed explanation of the Hadoop Framework and its components.
- Understanding HDFS and the processes for reading from 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, carefully addressing network configurations, starting Hadoop Daemons, and testing the cluster).
- Understanding the MapReduce Framework and its operational mechanics.
- Executing MapReduce jobs on a Hadoop cluster.
- Grasping 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.
What is MapR and Why Choose MapR:
- An overview of MapR and its architecture.
- Understanding and working with the MapR Control System, MapR Volumes, snapshots, and Mirrors.
- Planning a cluster specifically within the context of MapR.
- Comparing MapR with other distributions and Apache Hadoop.
- MapR installation and cluster deployment procedures.
Cluster Setup & Administration:
- Managing services, nodes, snapshots, mirrored volumes, and remote clusters.
- Understanding and managing Nodes.
- Familiarity with Hadoop components and installing them alongside MapR Services.
- Accessing data on the cluster, including via NFS, while 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 Native storage for MapR tables.
- Cluster configuration and tuning for optimum performance.
Cluster Upgrade and Integration with Other Setups:
- Upgrading the MapR software version and understanding different types of upgrades.
- Configuring the MapR cluster to access an HDFS cluster.
- Setting up a MapR cluster on Amazon Elastic MapReduce.
All the aforementioned topics include demonstrations and practice sessions to provide learners with hands-on experience with the technology.
Requirements
- Fundamental 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
