Learn Hadoop Architecture and Key Components – Quick Guide

In today’s world, managing and processing large amounts of data is very important for businesses in many areas. Hadoop is an open-source tool the Apache Software Foundation created to help with big data problems. It works by breaking large datasets into smaller pieces and spreading them across different computers with the help of Hadoop commands. It also makes it easier to store and handle both structured and unstructured data. Hadoop has a strong structure with key parts like HDFS, MapReduce, and YARN. Which makes it popular in industries such as finance, healthcare, and e-commerce. So, this guide will explore the basics of Hadoop architecture, its history, and how it is used in real-life situations.

Introduction to Hadoop

Hadoop is an open-source tool the Apache Software Foundation created to store and process huge amounts of data. It works by breaking the data into smaller pieces and spreading them across many computers, making it easy to manage large datasets. Hadoop handles structured and unstructured data well, making it perfect for big data tasks. It has four main parts as well and Hadoop architecture is widely used in industries like finance and healthcare. Also in e-commerce, it works well on low-cost computers and can grow to meet large data needs.

History of Hadoop

Hadoop's history began in the early 2000s, inspired by Google's ideas on how to store and process large amounts of data. In 2005, Doug Cutting and Mike Cafarella created Hadoop, naming it after a toy elephant. It was first made to help the Nutch search engine, but soon many others saw its value. In 2006, Hadoop became a part of the Apache Software Foundation. As well as Yahoo became one of the first big companies to use it for handling huge data. Yahoo's use helped Hadoop grow quickly. Today, Hadoop is a widely used open-source tool for managing big data in many industries.

What is Hadoop Architecture?

It is built to handle large amounts of data and process it quickly using multiple computers. It works on a master-slave system, where HDFS is used for storing data and MapReduce for processing it. This architecture is also designed to be fault-tolerant (able to handle failures) and can easily grow as more data is added.

Hadoop Architecture Diagram

In a Hadoop setup, there are Master Nodes and Slave Nodes. The Master Node controls how data and tasks are shared, while Slave Nodes store and process the data. Hadoop also makes copies of data to prevent loss in case a computer fails, ensuring reliability.

Key Components of Hadoop Architecture

The architecture is designed for distributed data storage and parallel processing across clusters of computers. It primarily consists of the following key components of the Apache Hadoop framework:

Hadoop Distributed File System (HDFS)

HDFS is Hadoop's main system for storing data. It splits the data into smaller pieces and spreads them across many computers. Also, this makes it easier to store large amounts of data and keeps it safe if something goes wrong. As well as a key feature of HDFS is replication. This means it automatically makes copies of the data to prevent any loss if a computer fails.

MapReduce

MapReduce is the part of Hadoop that processes data. It takes a big job and breaks it into smaller tasks called Map tasks. Which run at the same time on different computers. After processing, it combines the results in a step called Reduce. This way of working also allows MapReduce to handle large amounts of data quickly, making it great for big data processing.

Yet Another Resource Negotiator (YARN)

YARN is the part of Hadoop architecture that manages resources. It helps decide how much computing power each job gets and makes sure multiple applications can run at the same time. By separating how tasks are scheduled from how resources are managed. As well as YARN makes it easier to scale and adapt Hadoop to different needs.

Hadoop Common

Hadoop Common is a set of shared tools and libraries that support other parts of Hadoop. It also helps different components of Hadoop work well together and ensures they can communicate easily.

Modules of Hadoop

Hadoop has several modules that work together to store and process large amounts of data. So, here are the main modules of Hadoop architecture:

• Hadoop Distributed File System (HDFS): Manages how data is stored across many computers.
• MapReduce: Processes large datasets by breaking them into smaller tasks and running them at the same time.
• YARN: Manages and gives out the resources needed for data processing.
• Hadoop Common: Provides the essential tools as well as libraries needed for the other Hadoop modules to work.

In short, these modules combine to create a complete system for handling big data storage, processing, and analysis.

Advantages of Hadoop

Hadoop is a powerful framework that enables the distributed processing of large data sets across clusters of computers using simple programming models. Here are some of the key advantages of Hadoop architecture:

• Scalability: You can easily add more computers (nodes) to handle more data.
• Fault Tolerance: Hadoop keeps copies of data on different computers, so no data is lost if one computer fails.
• Cost-Effective: Hadoop is free to use because it’s open-source, and it can run on regular, inexpensive hardware, saving money on infrastructure.
• High Throughput: Hadoop can analyze data quickly because it processes tasks at the same time across many computers.
• Flexibility: Hadoop can work with different types of data, like organized, and semi-organized. As well as unorganized data, it is very useful for data analysis.

Hadoop use cases

Hadoop is used in many industries for managing large amounts of data. So, here are some common uses:

• Data Warehousing: Companies use Hadoop to store and analyze large data sets for making better business decisions.
• Log Processing: It can also handle big log files, helping organizations understand system activity and find problems.
• Search Engines: Hadoop architecture helps search engines by processing large amounts of data for indexing websites.
• Recommendation Systems: Online stores use Hadoop to give personalized suggestions to customers based on their behavior.
• Healthcare: In healthcare, Hadoop manages as well as analyzes large health data to improve patient care, research, and diagnoses.

Also Read: Top 9 Machine Learning Applications in Healthcare

Important Hadoop commands List

Here is a list of essential commands that help in managing data and performing various tasks within the framework.

1. hadoop fs -ls: Lists files in the Hadoop file system.
2. hadoop fs -put <local_path> <hdfs_path>: Copies files from local storage to HDFS.
3. hadoop fs -get <hdfs_path> <local_path>: Retrieves files from HDFS to local storage.
4. hadoop fs -rm <path>: Deletes a file from HDFS.
5. hadoop fs -mkdir <path>: Creates a directory in HDFS.
6. hadoop fs -du <path>: Displays disk usage statistics of HDFS directories.
7. hadoop fs -cat <path>: Displays the content of a file in HDFS.

Hadoop Data Types

When working with Hadoop, particularly with its ecosystem, understanding data types is essential for data processing and storage. Here are the primary data types commonly used in Hadoop architecture:

1. Primitive Data Types

• IntWritable: A type that represents a 32-bit signed whole number.
• LongWritable: For a 64-bit signed whole number.
• FloatWritable: For a 32-bit decimal number (single-precision).
• DoubleWritable: For a 64-bit decimal number (double-precision).
• Text: A type that represents a string of text in UTF-8 format.
• BooleanWritable: Represents a true or false value.

2. Complex Data Types

• ArrayWritable: A type that holds an array of writable objects (multiple values of the same type).
• MapWritable: Represents a map of keys and values, where both can be writable objects.
• StructWritable: A type that also can hold multiple fields of different types in a data structure.

3. Custom Writable Data Types

• You can create your types by following the Writable interface. This helps manage more complex data structures.

4. Hadoop File Formats

• Text File: A regular text file with UTF-8 encoded text.
• Sequence File: A file that stores binary key/value pairs, optimized for moving data between MapReduce jobs.
• Avro: A framework for serializing data that allows for dynamic typing.
• Parquet: A file format created for efficient data processing, especially with tools like Apache Spark and Hive.
• ORC (Optimized Row Columnar): A format manufactured for big data processing with Hive, offering good compression and fast performance.

These data types allow for the efficient handling of data during the MapReduce processing.

Conclusion

In conclusion, Hadoop architecture is an important tool for handling and processing large amounts of data today. Its strong structure includes HDFS, MapReduce, YARN, and Hadoop Common. Which helps organizations store, process, and analyze data efficiently. Hadoop’s benefits, such as being easy to scale and able to recover from failures. As well as low cost, make it popular in many industries. It is used in various ways, from storing business data to analyzing health data. Learning about Hadoop's data types and key commands makes it even more useful. As big data grows, knowing how to use Hadoop will be crucial for people who want to make smart decisions based on data.

Frequently Asked Questions (FAQs)