Hadoop tutorial for Freshers,
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Apache Hadoop is an open-source framework for distributed processing of large datasets across clusters of computers, designed to handle big data challenges like scalability and fault tolerance. It consists of two main components: the Hadoop Distributed File System (HDFS) for data storage and MapReduce for data processing. While it offers advantages such as computing power and low cost, it also has drawbacks in terms of integration, ease of use, and security.
Hadoop tutorial for Freshers,
- 1. BY – Arvind BEST IT TRAINING INSTITUTE IN BANGALORE
- 2. What is Hadoop? • The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. • It is made by apache software foundation in 2011. • Written in JAVA.
- 3. Hadoop is open source software. Framework Massive Storage Processing Power
- 4. Big Data •Big data is a term used to define very large amount of unstructured and semi structured data a company creates. •The term is used when talking about Petabytes and Exabyte of data. •That much data would take so much time and cost to load into relational database for analysis. •Facebook has almost 10billion photos taking up to 1Petabytes of storage.
- 5. So what is theproblem?? 1. Processing that large data is very difficult in relational database. 2. It would take too much time to process data and cost.
- 6. We can solve this problem by Distributed Computing. But the problems in distributed computing is – Hardware failure Chances of hardware failure is always there. Combine the data after analysis Data from all disks have to be combined from all the disks which is a mess.
- 7. ToSolve all the Problems HadoopCame. It has two main parts – 1. Hadoop Distributed File System (HDFS), 2. Data Processing Framework & MapReduce
- 8. 1. Hadoop Distributed File System It ties so many small and reasonable priced machines together into a single cost effective computer cluster. 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. it automatically stores multiple copies of all data. It provides simplified programming model which allows user to quickly read and write the distributed system.
- 9. 2. MapReduce MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a cluster. It is an associative implementation for processing and generating large data sets. MAP function that process a key pair to generates a set of intermediate key pairs. REDUCE function that merges all intermediate values associated with the same intermediate key
- 12. Pros of Hadoop 1. Computing power 2. Flexibility 3. Fault Tolerance 4. Low Cost 5. Scalability
- 13. Cons of Hadoop 1. Integration with existing systems Hadoop is not optimised for ease for use. Installing and integrating with existing databases might prove to be difficult, especially since there is no software support provided. 2. Administration and ease of use Hadoop requires knowledge of MapReduce, while most data practitioners use SQL. This means significant training may be required to administer Hadoop clusters. 3. Security Hadoop lacks the level of security functionality needed for safe enterprise deployment, especially if it concerns sensitive data.
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