The Hadoop framework transparently provides both reliability and data motion to applications. Hadoop implements a computational paradigm named MapReduce, where the application is divided into many small fragments of work, each of which can execute or re-execute on any node in the cluster. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both map/reduce and the distributed file system are designed so that node failures are automatically handled by the framework. It enables applications to work with thousands of computation-independent computers and petabytes of data. The entire Apache Hadoop platform is now commonly considered to consist of the Hadoop kernel, MapReduce and Hadoop Distributed File System (HDFS), as well as a number of related projects including Apache Hive, Apache HBase, and others.
Hadoop is written in the Java programming language and is an Apache top-level project being built and used by a global community of contributors. Hadoop and its related projects (Hive, HBase, Zookeeper, and so on) have many contributors from across the ecosystem. Though Java code is most common, any programming language can be used with "streaming" to implement the "map" and "reduce" parts of the system.
Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that's a key consideration.
Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.
MapReduce programming is not a good match for all problems. It’s good for simple information requests and problems that can be divided into independent units, but it's not efficient for iterative and interactive analytic tasks. MapReduce is file-intensive. Because the nodes don’t intercommunicate except through sorts and shuffles, iterative algorithms require multiple map-shuffle/sort-reduce phases to complete. This creates multiple files between MapReduce phases and is inefficient for advanced analytic computing.
There’s a widely acknowledged talent gap. It can be difficult to find entry-level programmers who have sufficient Java skills to be productive with MapReduce. That's one reason distribution providers are racing to put relational (SQL) technology on top of Hadoop. It is much easier to find programmers with SQL skills than MapReduce skills. And, Hadoop administration seems part art and part science, requiring low-level knowledge of operating systems, hardware and Hadoop kernel settings.
Data security. Another challenge centers around the fragmented data security issues, though new tools and technologies are surfacing. The Kerberos authentication protocol is a great step toward making Hadoop environments secure.
Full-fledged data management and governance. Hadoop does not have easy-to-use, full-feature tools for data management, data cleansing, governance and metadata. Especially lacking are tools for data quality and standardization.
Characteristics of Big Data
Why is parallel computing important
Discuss various products developed by vendors
Components of Hadooop
Identify various processes
Basic file commands
Web Based User Interface
Reading & Writing to files
Run a word count program
View jobs in the Web UI
Types of installation (RPM’s & Tar files)
Set up ‘ssh’ for the Hadoop cluster
XML, masters and slaves files
Checking system health
Discuss block size and replication factor
Benchmarking the cluster
Adding and de-commissioning nodes
Purpose of secondary name node
Recovery from a failed name node
Hadoop infrastructure monitoring
Hadoop specific monitoring
Install and configure Nagios / Ganglia
Discuss Hive, Sqoop, Pig, HBase, Flume
Use cases of each
Use Hadoop streaming to write code in Perl / Python