Big Data refers to massive datasets that grow exponentially and come from a variety of sources, presenting challenges in handling, processing and analysis. These datasets can be structured, unstructured or semi-structured. To effectively manage this data Hadoop comes into the picture. Let's dive into Big Data and how Hadoop revolutionizes data processing.
The objective of this tutorial is to help you understand Big Data and Hadoop, its evolution, components and how it solves the problems of managing large, complex datasets. By the end of this tutorial, you will have a clear understanding of Hadoop's ecosystem and its key functionalities, from setup to processing large datasets.
What is Big Data?
In this section, we will explore what Big Data means and how it differs from traditional data. Big Data is characterized by its large volume, high velocity and diverse variety, making it difficult to process with traditional tools.
What is Hadoop ?
Hadoop is an open-source framework written in Java that allows distributed storage and processing of large datasets. Before Hadoop, traditional systems were limited to processing structured data mainly using RDBMS and couldn't handle the complexities of Big Data. In this section we will learn how Hadoop offers a solution to handle Big Data.
Installation and Environment Setup
Here, we’ll guide you through the process of installing Hadoop and setting up the environment on Linux and Windows.
Components of Hadoop
In this section, we will explore HDFS for distributed and fault-tolerant data storage, MapReduce programming model for data processing and YARN for resource management and job scheduling in a Hadoop cluster.
Understanding Cluster, Rack and Schedulers
We will explain the concept of clusters, rack awareness and job schedulers in Hadoop which ensure optimal resource utilization and fault tolerance.
Understanding about HDFS
In this section, we will cover various file systems supported by Hadoop including HDFS, its large block sizes for improved performance, Hadoop daemons (like NameNode and DataNode) and their roles, file block replication for data reliability and the process of data reading involving the client, NameNode and DataNode.
Understanding about MapReduce
In this section, we will explore the MapReduce model, its architecture including Mapper, Reducer and JobTracker, the roles of Mapper and Reducer in processing and aggregating data and the execution flow of a MapReduce job from submission to completion.
MapReduce Programs
In this section, we will provide examples of real-world MapReduce programs such as weather data analysis and character count problems.
Hadoop Streaming
In this section, we will explain Hadoop Streaming, a utility that allows using languages like Python for MapReduce tasks and demonstrate its usage with a Word Count problem example.
Hadoop File and Commands
In this section, we will cover Hadoop file commands including file permissions and ACLs, the copyFromLocal command for transferring files and the getmerge command for merging output files in HDFS.
More about Hadoop
In this section, we will explore what's new in Hadoop Version 3.0, the top reasons to learn Hadoop, popular Hadoop analytics tools for Big Data, recommended books for learning Hadoop, its key features that make it popular and compare Hadoop with Spark and Flink.
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