Hadoop Basics

HDFS creates multiple replicas of data blocks and stores them on different nodes. If a node containing a replica of a data block goes down, the system can still function because the data is available on other nodes.

What is Hadoop?

Hadoop is an Apache open-source software platform for distributed storage and distributed computing based on java. It enables applications to work with thousands of nodes and petabytes of data. Hadoop provides a Java API for programming.

The Hadoop Distributed File System (HDFS) is the primary file system used by Hadoop applications. It is designed to be scalable and fault tolerant.

Hadoop consists of two main components: the Hadoop Distributed File System (HDFS) and the MapReduce programming model.

HDFS is a file system that stores data on commodity hardware. It is designed to be scalable and fault tolerant.

MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.

How does Hadoop work?

Hadoop is a system that allows for the distributed processing of large data sets across clusters of computer nodes. It is designed to be scalable, reliable, and flexible.

Hadoop works by breaking up large data sets into smaller pieces that are then distributed across the nodes in a cluster. Each node in the cluster then processes the data in parallel and the results are then brought back together to produce a final output.

The key to Hadoop’s success is its ability to handle large amounts of data efficiently. When traditional relational databases run into scalability problems, Hadoop can be used to provide a more efficient solution.

What are the benefits of using Hadoop?

Hadoop is an open source framework that helps organizations manage and process large data sets. It is designed to be scalable, reliable, and easy to use. Hadoop makes the system more resilient by replicating data across multiple nodes. This ensures that if one node goes down, the data will still be available on other nodes. Hadoop is also designed to be flexible, so it can be used for a variety of tasks, such as data warehousing, web indexing, and log processing.

Setting up Hadoop

Hadoop is a system that is built to be resilient. It is used to manage and process big data. When you set it up, it will be able to handle more data and be more reliable.

Install Hadoop

Hadoop is an Apache project that is used for processing and storing large data sets. It is very efficient in handling a lot of data, and it can be used on a cluster of commodity servers. Hadoop is written in Java, and it runs on Linux systems.

This document will describe how to install Hadoop on a single node. You will need root access to the system in order to install Hadoop.

1) Download the latest version of Hadoop from the Apache website:
http://hadoop.apache.org/releases.html

2) Extract the contents of the tar file:
tar xzf hadoop-X.Y.Z.tar.gz

3) Move into the extracted directory:
cd hadoop-X.Y.Z/

4) Edit the configuration files in the conf/ directory:
vi conf/hadoop-env.sh # Set JAVA_HOME variable
vi conf/core-site.xml # Set fs.default.name property

Configure Hadoop

Installing Hadoop can be complex, depending on your system and network configuration. Apache Hadoop requires a working Java installation.

If you want to run Hadoop on a single machine, you only need to set up a dedicated server with enough RAM and CPU cores to support your planned usage. For larger deployments, it is recommended that you configure a dedicated Hadoop cluster using multiple servers.

To configure Hadoop, you need to edit the following files:

• hadoop-env.sh: This file contains environment variables that need to be set for Hadoop to work properly. You will at least need to set the JAVA_HOME variable here.
• core-site.xml: This file contains configuration parameters for the Hadoop core, such as the namenode and jobtracker hosts.
• mapred-site.xml: This file contains configuration parameters for MapReduce, such as the host for the tasktracker service.
• hdfs-site.xml: This file contains configuration parameters for the HDFS distributed filesystem, such as the replication factor and block size.
  Run Hadoop
  
  There are a few ways to run Hadoop:

1. Run it on a single machine – Perfect for experimentation and learning Hadoop.
2. Run it on a cluster – This is the most common way to run Hadoop in production as it allows for scalability and resilience.
3. Run it in the cloud – This has become a popular option as it can reduce costs and is more flexible than running Hadoop on your own hardware.

If you want to run Hadoop on a single machine, you can use the standalone mode which comes with the Hadoop release. Standalone mode is perfect for experimentation and learning, but it is not suitable for production use as it does not provide scalability or resilience.

If you want to run Hadoop in production, you will need to set up a cluster. A Hadoop cluster consists of a number of machines, called nodes, that are connected together. There are two types of nodes in a Hadoop cluster:

1. Master nodes – these manage the file system and jobTracker
2. Slave nodes – these store data and perform computations

You will need to install Hadoop on all of the machines in your cluster before you can start using it. Once Hadoop is installed, you will need to configure the master node and each slave node. The configuration files are located in the $HADOOP_HOME/conf directory.

Using Hadoop

Hadoop is a distributed system that can be used for storing and processing large data sets. It is an open source project that is maintained by the Apache Software Foundation. Hadoop makes the system more resilient by using a technique called replication. Replication is the process of copying data from one place to another.

Create a Hadoop user

Hadoop services are run by the superuser, so you first need to create a Hadoop user. You can use any name for the user, but for simplicity we’ll use ‘hadoop’.

groupadd hadoop
useradd -g hadoop hadoop

Log in to the server as the new user and set up a password.

su – hadoop
passwd
Enter a secure password for the user and confirm it when prompted.

Access the Hadoop file system

In Hadoop, the file system is called HDFS. To access the file system from a Hadoop cluster you will use a tool called the Hadoop fs (filesystem) shell. The shell is a Unix-like environment that supports common commands such as ls, cat, cp, mv, pwd, and mkdir. You can also use most of the common Unix/Linux utilities such as grep, awk, and sed with the Hadoop fs shell.

To access the Hadoop fs shell, you will need to SSH into the NameNode of your Hadoop cluster. Once you are logged in, you can start using the shell by typing hadoop fs at the command prompt.

The following are some of the most common commands that are used with the Hadoop fs shell:

ls: Lists files in a directory
mkdir: Makes a new directory
cp: Copies files from one location to another
mv: Moves files from one location to another
rm: Deletes files
cat: Prints contents of a file to standard output
expunge: Empty Trash

Use Hadoop to process data

Hadoop is an open source software framework that enables distributed storage and processing of large data sets across a cluster of commodity servers. It is designed to be scalable, fault-tolerant and easy to use.

Hadoop can be used to process structured and unstructured data in a variety of formats including log files, text files, images and videos. It can also be used to process streaming data in real-time.

Hadoop is well suited for use cases where you need to process large amounts of data in parallel, such as financial analysis, web indexing, social network analysis, genomics and machine learning.

Hadoop Administration

Hadoop Administration is the process of managing the Hadoop ecosystem. It includes tasks like configuring, monitoring, and securing the Hadoop cluster. The Hadoop administrator also needs to perform regular maintenance tasks like upgrading the Hadoop version, patching the system, and so on.

Hadoop daemons

Hadoop daemons are the individual services that run on a node in a Hadoop cluster. Each daemon handles a specific role and responsibility within the cluster. There are four main types of Hadoop daemons:

-NameNode: The NameNode is the centerpiece of an HDFS file system. It manages the file system namespace and regulates access to files by clients. It does not store actual data blocks of files, but stores metadata about the files including block locations.

-DataNode: DataNodes are the workhorses of an HDFS file system. They store actual data blocks of files and perform computations on them when required by clients. They report back to the NameNode periodically with information about the status of their storage, so that the NameNode can keep track of where file data is located.

-SecondaryNameNode: The SecondaryNameNode is not a true standby for the NameNode, but rather performs important maintenance tasks for the NameNode such as periodic checkpointing and logfile analysis. By taking on these responsibilities, it helps to offload some work from the primary NameNode and keeps it running more smoothly.

-ResourceManager: The ResourceManager is responsible for managing resources across the cluster and scheduling applications to run on them. It interacts with both application masters and NodeManagers in order to achieve this goal.

Hadoop configuration files

Hadoop configuration files are XML files that define the configuration of a Hadoop cluster. The two main types of files are:
-Core-site.xml: Contains configurations for the Hadoop core, such as the I/O system, security settings, and buffer sizes.
-hdfs-site.xml: Contains configurations for the Hadoop Distributed File System (HDFS), such as the block size, replication factor, andNameNode URI.

There are also several other important files, such as mapred-site.xml (for MapReduce configuration), yarn-site.xml (for YARN configuration), slaves (a list of slave nodes in the cluster), and masters (a list of master nodes in the cluster).

Hadoop security

Hadoop security is the legislation, policies, procedures and technical measures used to protect electronic data stored on computer networks. The goal of Hadoop security is to minimize the risks associated with unauthorized access, disclosure, destruction or modification of data.

Hadoop security tools and techniques include user authentication, authorization, data encryption and activity logging. User authentication verifies the identity of users who access data. Authorization controls what users are allowed to do with the data. Data encryption protects information from being read by unauthorized users. Activity logging tracks user activity on the network.

Hadoop security is a concern for organizations that use or plan to use Hadoop in their environment. Hadoop is an open source software framework that enables distributed storage and processing of large data sets across a cluster of commodity servers. Hadoop is often used to store and process sensitive information, such as customer records, financial transactions and medical records.

Organizations that use Hadoop must implement security controls to protect their data. The Apache Hadoop project includes some basic security features, but these features are not sufficient for most organizations. Organizations should consider implementing additional security controls, such as access control lists (ACLs) and Kerberos authentication.