Introduction:

Hadoop is an open source platform that is highly popular these days. Every corporation is hunting for a skilled Hadoop developer.

The Hadoop was first introduced by Mike Cafarella and Doug Cuttingwho  who are the persons trying to create a search engine system that can index 1 billion pages. So they are going to start their research to build that search engine but they found that the cost for preparing such engine will be very high. So they stumbled upon a document released in 2003 that outlined the design of Google's distributed file system, known as GFS. Later, in 2004, Google published another article that introduced MapReduce to the public. Finally, these two investigations formed the basis for the "Hadoop" framework.

How It All Started?

As previously noted, the cornerstone of Hadoop and its relevance in terms of search data processing. The word GFS is something that is mostly utilized to solve all search strategies for keeping very huge files created as a part of the web. In any case, there is a crawling and indexing procedure.

 It also uses the  MapReduce technique which is mainly used to map the large data files and help for better search strategy. It is recognized as an important component of the "Hadoop" system.

So, by considering it the Hadoop is became so powerful. 

What is Big Data?

As previously stated, Hadoop is open source. It is primarily a Java-based system designed for storing and analyzing large amounts of data. The data is stored on low-cost commodity servers that operate as clusters. Cafarella, Hadoop employs the MapReduce programming approach to store and retrieve data from its nodes more quickly.

Big data refers to a collection of enormous datasets that cannot be handled using conventional computer approaches. It is no more just a technique or a tool; rather, it has evolved into a comprehensive subject including a variety of tools, approaches and frameworks.

Here the Big data is mainly used to involves the data produced by different devices and applications. Some of the disciplines that fall within the category of Big Data are covered below.

Black Box Data − It is a component which is get used as a part of helicopter, airplanes, and jets, etc.is primarily utilized to record the flight crew's voices and information, as well as recordings from microphones and headphones in the aircraft.

Social media data is a key aspect in the growth of Big Data since it gives information about people's behavior.

Stock Exchange Data − This data contains information regarding customers' 'buy' and'sell' choices on shares of various firms.

Power Grid Data refers to the information used by a specific node in relation to a base station.

Transport data refers to the model, capacity, distance, and availability of vehicles.

Search Engine Data − Search engines retrieve data from several databases. 

Nowadays, we use so many social media programs. Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe which is a real time example of Big Data.

Big Data & Hadoop – Restaurant Analogy:

Let us now use a restaurant example to better appreciate the challenges related with Big Data. Here we will try to learn that how Hadoop solved that problem.

Let us consider that the Bob is a businessman who has opened a small restaurant. Initially, he received two orders each hour in his restaurant, and he had one chef and one food shelf to manage all of them.

Let us now compare the restaurant example to the typical case, in which data is created at a consistent pace and our existing systems, such as RDBMS, are capable of handling it, much like Bob's chef.

Similarly, numerous processing units were deployed to analyze large data sets in concurrently (much as Bob employed four cooks). Even in this situation, bringing in several processing units was ineffective since the centralized storage unit became the bottleneck.

In other words, the central storage unit's performance determines the whole system performance. As a result, if our central store fails, the entire system becomes vulnerable. As a result, this single point of failure had to be addressed once more.

Hadoop File System was created utilizing a distributed file system design. It runs on commodity hardware. Compared to other distributed systems, HDFS is more fault-tolerant and constructed utilizing low-cost hardware.

To address the storage and processing issues, two essential Hadoop components were created: HDFS and YARN. HDFS addresses the storage issue since it stores data in a distributed manner and is easily expandable. YARN solves the processing problem by significantly lowering processing time. Moving on, what is Hadoop?

What is Hadoop?

As previously said, Hadoop is an open-source software framework similar to Java script that is mostly used for distributed Big Data storage and processing over large clusters of commodity hardware. Hadoop is released under the Apache 2 license.

Hadoop was created based on Google's MapReduce article and incorporates functional programming techniques. Hadoop is one of Apache's most advanced projects, written in Java. Hadoop was founded by Doug Cutting and Michael J. Cafarella.

Hadoop-as-a-Solution:

Let's look at how Hadoop solves the Big Data concerns we've explored so far.

The main issues with big data are as follows:

• Capturing data

• Curation

• Storage

• Searching

• Sharing

• Transfer

• Analysis

• Presentation

How to store huge amount of data:

1. Hadoop's major component is the HDFS system, which provides a distributed approach to store Big Data

2. Here the data is stored in blocks in DataNodes and you specify the size of each block. 

3. For example, suppose you have 512 MB of data and have set HDFS to produce 128 MB of data blocks.

4. The HDFS will partition data into four blocks (512/128=4) and store them across many DataNodes.

5. To ensure fault tolerance, data blocks are duplicated on separate DataNodes while being stored in them.

How to store a variety of data:

1. HDFS in Hadoop is capable of storing any types of data, whether structured, semi-structured, or unstructured.

2. HDFS, there is no pre-dumping schema validation. 

3. It also adheres to the principle of "write once, read many."

4. Because of this, you can simply write any type of data once and read it several times to gain insights.

How to process the data faster:

In this situation, Hadoop may relocate the processing unit to the data rather of the other way around.

So, what does it mean to move the compute unit to data?

It implies that instead of transporting data from multiple nodes to a single master node for processing, the processing logic is given to the nodes where the data is stored, allowing each node to handle a portion of the data simultaneously. Finally, all of the intermediary output generated by each node is combined, and the final answer is returned to the client.

Features of Hadoop:

The following properties distinguish Hadoop from others. Such as

• It is ideal for distributed storage and processing.

• Hadoop provides a command-line interface for interacting with HDFS.

• The built-in servers for namenode and datanode allow users to simply verify the status of the cluster.

• Streaming access to file system data.

• HDFS provides file permissions and authentication.

Reliability

Hadoop architecture was developed in such a way that it includes built-in fault tolerance characteristics. So that even if a failure occurs, there is a backup method in place to deal with the problem. hence, Hadoop is highly reliable.

Economical

Hadoop uses commodity hardware (like your PC, laptop). We can easily execute it in any environment.

For example, in a modest Hadoop cluster, all DataNodes can have standard specifications such as 8-16 GB RAM, 5-10 TB hard drive, and Xeon CPUs.

It is also easier to manage a Hadoop system and more cost-effective. Furthermore, Hadoop is open-source software, thus there are no license fees.

Scalability 

Hadoop offers the inherent capacity to integrate smoothly with cloud-based services. So, if you deploy Hadoop in the cloud, you won't have to worry about scalability.

Flexibility 

Hadoop is extremely adaptable in terms of its capacity to handle various types of data. Hadoop can store and analyze many types of data, including structured, semi-structured, and unstructured data.

Hadoop Core Components:

While configuring a Hadoop cluster, you have the choice of selecting a number of services as part of your Hadoop platform, however there are two services that are always mandatory for setting up Hadoop. 

HDFS

Let us go ahead with HDFS first. HDFS's major components are the NameNode and the DataNode. Let's go over the duties of these two components in depth.

HDFS uses the master-slave architecture and includes the following components.

Namenode

1. The namenode is the commodity hardware that houses the GNU/Linux operating system and namenode software.

2. It is software that can be executed on standard hardware.

3. The machine with the namenode operates as the master server and performs the following tasks: Manages the file system namespace and controls client access to files.

4. It can also perform file system operations including renaming, shutting, and opening files and directories.

5. If a file is removed in HDFS, the NameNode instantly records it in the EditLog.

6. It routinely gets a Heartbeat and a block report from all the DataNodes in the cluster to check that they are alive.

7. It maintains track of all the blocks in the HDFS and DataNode where they are stored

Datanode:

1. The datanode is a piece of commodity hardware that runs the GNU/Linux operating system and datanode software.

2. Every node (commodity hardware/system) in a cluster will have a datanode. These nodes control the system's data storage.

3. Datanodes execute read-write operations on file systems based on client requests.

4. They also conduct block formation, deletion, and replication in accordance with the namenode's commands.

5. It is also responsible for producing, removing, and replicating blocks based on the NameNode's choices.

6. It transmits heartbeats to the NameNode on a regular basis to indicate the overall health of HDFS; the default frequency is 3 seconds.

Block

1. In general, HDFS files are used to store user data.

2. A file in a file system will be partitioned into one or more segments and/or stored in separate data nodes.

3. These file segments are referred to as blocks. A Block is the minimal quantity of data that HDFS may read or write.

4. The default block size is 64MB, although it can be adjusted depending on the HDFS settings.

YARN

Hadoop's YARN consists of two primary components, which include:

1.  ResourceManager and 

2. NodeManager.

ResourceManager

1. It is a cluster-level component (one per cluster) that operates on the master computer.

2. It manages resources and schedules apps that operate on top of YARN.

3. It consists of two components: the Scheduler and the ApplicationManager.

4. The Scheduler allocates resources to the many operating apps.

5. The ApplicationManager is in charge of receiving job submissions and negotiating the first container for running the application.

6. It monitors the heartbeats from the Node Manager.

7. NodeManager

8. It is a node-level component (one on per node) that runs on all slave machines.

9. It is in charge of maintaining containers and monitoring resource use in each one.

10. It also monitors node health and log management.

11. It connects with ResourceManager on a constant basis in order to stay current. 

Hadoop Ecosystem

Hadoop is a platform or framework for solving Big Data challenges. It serves as a package of services for consuming, storing, and analyzing large data collections, as well as configuration management tools.

Fault detection and recovery − HDFS's use of commodity hardware leads to frequent component failures. As a result, HDFS should have systems for detecting and recovering faults quickly and automatically.

HDFS should contain hundreds of nodes per cluster to manage applications with large datasets.

When computation occurs near the data, it is more efficient to complete the required job. It decreases network traffic while increasing throughput, particularly for large datasets.

Last.FM Case Study

1. Last.FM, an online radio and community-driven music discovery service, was created in 2002.

2. Here, the user sends information to the Last.FM servers identifying the songs they are listening to.

3. The supplied data is processed and saved so that the user may view it as charts.

4. Scrobble: When a user plays a track of his or her own choosing and provides the information to Last.FM via a client application.

5. Radio listen: When a user listens to a Last.FM radio station and streams a song. 

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