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Introduction to Hadoop

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  • Last Updated : 25 Jan, 2023
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INTRODUCTION:

Hadoop is an open-source software framework that is used for storing and processing large amounts of data in a distributed computing environment. It is designed to handle big data and is based on the MapReduce programming model, which allows for the parallel processing of large datasets.

Hadoop has two main components:

  • HDFS (Hadoop Distributed File System): This is the storage component of Hadoop, which allows for the storage of large amounts of data across multiple machines. It is designed to work with commodity hardware, which makes it cost-effective.
  • YARN (Yet Another Resource Negotiator): This is the resource management component of Hadoop, which manages the allocation of resources (such as CPU and memory) for processing the data stored in HDFS.
  • Hadoop also includes several additional modules that provide additional functionality, such as Hive (a SQL-like query language), Pig (a high-level platform for creating MapReduce programs), and HBase (a non-relational, distributed database).
  • Hadoop is commonly used in big data scenarios such as data warehousing, business intelligence, and machine learning. It’s also used for data processing, data analysis, and data mining.

What is Hadoop?

Hadoop is an open source software programming framework for storing a large amount of data and performing the computation. Its framework is based on Java programming with some native code in C and shell scripts.

Hadoop is an open-source software framework that is used for storing and processing large amounts of data in a distributed computing environment. It is designed to handle big data and is based on the MapReduce programming model, which allows for the parallel processing of large datasets.

Hadoop has two main components:

  • HDFS (Hadoop Distributed File System): This is the storage component of Hadoop, which allows for the storage of large amounts of data across multiple machines. It is designed to work with commodity hardware, which makes it cost-effective.
  • YARN (Yet Another Resource Negotiator): This is the resource management component of Hadoop, which manages the allocation of resources (such as CPU and memory) for processing the data stored in HDFS.
  • Hadoop also includes several additional modules that provide additional functionality, such as Hive (a SQL-like query language), Pig (a high-level platform for creating MapReduce programs), and HBase (a non-relational, distributed database).
  • Hadoop is commonly used in big data scenarios such as data warehousing, business intelligence, and machine learning. It’s also used for data processing, data analysis, and data mining. It enables the distributed processing of large data sets across clusters of computers using a simple programming model.

History of Hadoop

Apache Software Foundation is the developers of Hadoop, and it’s co-founders are Doug Cutting and Mike Cafarella. It’s co-founder Doug Cutting named it on his son’s toy elephant. In October 2003 the first paper release was Google File System. In January 2006, MapReduce development started on the Apache Nutch which consisted of around 6000 lines coding for it and around 5000 lines coding for HDFS. In April 2006 Hadoop 0.1.0 was released.

Hadoop is an open-source software framework for storing and processing big data. It was created by Apache Software Foundation in 2006, based on a white paper written by Google in 2003 that described the Google File System (GFS) and the MapReduce programming model. The Hadoop framework allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. It is used by many organizations, including Yahoo, Facebook, and IBM, for a variety of purposes such as data warehousing, log processing, and research. Hadoop has been widely adopted in the industry and has become a key technology for big data processing.

Features of hadoop:

1. it is fault tolerance.

2. it is highly available.

3. it’s programming is easy.

4. it have huge flexible storage.

5. it is low cost.

Hadoop has several key features that make it well-suited for big data processing:

  • Distributed Storage: Hadoop stores large data sets across multiple machines, allowing for the storage and processing of extremely large amounts of data.
  • Scalability: Hadoop can scale from a single server to thousands of machines, making it easy to add more capacity as needed.
  • Fault-Tolerance: Hadoop is designed to be highly fault-tolerant, meaning it can continue to operate even in the presence of hardware failures.
  • Data locality: Hadoop provides data locality feature, where the data is stored on the same node where it will be processed, this feature helps to reduce the network traffic and improve the performance
  • High Availability: Hadoop provides High Availability feature, which helps to make sure that the data is always available and is not lost.
  • Flexible Data Processing: Hadoop’s MapReduce programming model allows for the processing of data in a distributed fashion, making it easy to implement a wide variety of data processing tasks.
  • Data Integrity: Hadoop provides built-in checksum feature, which helps to ensure that the data stored is consistent and correct.
  • Data Replication: Hadoop provides data replication feature, which helps to replicate the data across the cluster for fault tolerance.
  • Data Compression: Hadoop provides built-in data compression feature, which helps to reduce the storage space and improve the performance.
  • YARN: A resource management platform that allows multiple data processing engines like real-time streaming, batch processing, and interactive SQL, to run and process data stored in HDFS.

Hadoop Distributed File System

It has distributed file system known as HDFS and this HDFS splits files into blocks and sends them across various nodes in form of large clusters. Also in case of a node failure, the system operates and data transfer takes place between the nodes which are facilitated by HDFS.

HDFS

HDFS

Advantages of HDFS: It is inexpensive, immutable in nature, stores data reliably, ability to tolerate faults, scalable, block structured, can process a large amount of data simultaneously and many more. Disadvantages of HDFS: It’s the biggest disadvantage is that it is not fit for small quantities of data. Also, it has issues related to potential stability, restrictive and rough in nature. Hadoop also supports a wide range of software packages such as Apache Flumes, Apache Oozie, Apache HBase, Apache Sqoop, Apache Spark, Apache Storm, Apache Pig, Apache Hive, Apache Phoenix, Cloudera Impala.

Some common frameworks of Hadoop

  1. Hive- It uses HiveQl for data structuring and for writing complicated MapReduce in HDFS.
  2. Drill- It consists of user-defined functions and is used for data exploration.
  3. Storm- It allows real-time processing and streaming of data.
  4. Spark- It contains a Machine Learning Library(MLlib) for providing enhanced machine learning and is widely used for data processing. It also supports Java, Python, and Scala.
  5. Pig- It has Pig Latin, a SQL-Like language and performs data transformation of unstructured data.
  6. Tez- It reduces the complexities of Hive and Pig and helps in the running of their codes faster.

Hadoop framework is made up of the following modules:

  1. Hadoop MapReduce- a MapReduce programming model for handling and processing large data.
  2. Hadoop Distributed File System- distributed files in clusters among nodes.
  3. Hadoop YARN- a platform which manages computing resources.
  4. Hadoop Common- it contains packages and libraries which are used for other modules.

Advantages and Disadvantages of Hadoop

Advantages:

  • Ability to store a large amount of data.
  • High flexibility.
  • Cost effective.
  • High computational power.
  • Tasks are independent.
  • Linear scaling.

Hadoop has several advantages that make it a popular choice for big data processing:

  • Scalability: Hadoop can easily scale to handle large amounts of data by adding more nodes to the cluster.
  • Cost-effective: Hadoop is designed to work with commodity hardware, which makes it a cost-effective option for storing and processing large amounts of data.
  • Fault-tolerance: Hadoop’s distributed architecture provides built-in fault-tolerance, which means that if one node in the cluster goes down, the data can still be processed by the other nodes.
  • Flexibility: Hadoop can process structured, semi-structured, and unstructured data, which makes it a versatile option for a wide range of big data scenarios.
  • Open-source: Hadoop is open-source software, which means that it is free to use and modify. This also allows developers to access the source code and make improvements or add new features.
  • Large community: Hadoop has a large and active community of developers and users who contribute to the development of the software, provide support, and share best practices.
  • Integration: Hadoop is designed to work with other big data technologies such as Spark, Storm, and Flink, which allows for integration with a wide range of data processing and analysis tools.

Disadvantages:

  • Not very effective for small data.
  • Hard cluster management.
  • Has stability issues.
  • Security concerns.
  • Complexity: Hadoop can be complex to set up and maintain, especially for organizations without a dedicated team of experts.
  • Latency: Hadoop is not well-suited for low-latency workloads and may not be the best choice for real-time data processing.
  • Limited Support for Real-time Processing: Hadoop’s batch-oriented nature makes it less suited for real-time streaming or interactive data processing use cases.
  • Limited Support for Structured Data: Hadoop is designed to work with unstructured and semi-structured data, it is not well-suited for structured data processing
  • Data Security: Hadoop does not provide built-in security features such as data encryption or user authentication, which can make it difficult to secure sensitive data.
  • Limited Support for Ad-hoc Queries: Hadoop’s MapReduce programming model is not well-suited for ad-hoc queries, making it difficult to perform exploratory data analysis.
  • Limited Support for Graph and Machine Learning: Hadoop’s core component HDFS and MapReduce are not well-suited for graph and machine learning workloads, specialized components like Apache Giraph and Mahout are available but have some limitations.
  • Cost: Hadoop can be expensive to set up and maintain, especially for organizations with large amounts of data.
  • Data Loss: In the event of a hardware failure, the data stored in a single node may be lost permanently.
  • Data Governance: Data Governance is a critical aspect of data management, Hadoop does not provide a built-in feature to manage data lineage, data quality, data cataloging, data lineage, and data audit.

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