What is Hadoop?
Hadoop has come a long way since its birth and is growing continuously. It is important for anyone in the field of Big Data to know of the terminologies used in it and in this article we will discuss just that. However, before we discuss the technologies used in it let us first understand what Hadoop is.
It is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
However to understand the meaning we need to understand the terms used in the definition.
- Open-source software- In general, open source is any program whose source code is made available for use or modification as users or other developers see fit. Open source software is usually developed as a public collaboration and made freely available. Hadoop being an open-source software allows anyone to access it
- Framework-It means everything needed to develop and run software applications is provided – programs, connections, etc
- Massive storage-The framework breaks big data into blocks, which are stored on clusters of commodity hardware.
- Processing power- Hadoop concurrently processes large amounts of data using multiple low-cost computers for fast results
Let us now have a look at the top 10 Hadoop Technologies!
Apache Software Foundation
It is a decentralized open source community for developers. The ASF produced software is free and open source. ASF license protects Apache projects. This provides legal protection to volunteers who work on Apache products.
It is an essential part of Apache Hadoop framework. It is a collection of common utilities and libraries that support other Hadoop modules. Moreover, it contains the necessary Java Archive files and scripts required to start Hadoop.
It is an open source, sorted mapdata built on Hadoop. It’s base lies in Google’s big table. It has set of tables which keep data in key value format. Hbase suits well for sparse data sets which are very common in big data use cases.
HDFS, as the name suggests, is the File System for managing Apache Hadoop. It provides distributed storage by storing the large mounds of data across several machines. It is highly fault tolerant, and stores data in a very redundant manner, ensuring there is no data loss in case of a failure.
This is a service within Hadoop. It receives requests for MapReduce execution from the client. It then talks to the NameNodes to determine the location of the data and finds the best Task Tracker nodes to execute tasks based on data locality. Following this, it monitors the individual Task Trackers and then submits back the overall status of the job back to the client.
It is a programming model suitable for processing hundreds or thousands of servers in a Hadoop cluster. MapReduce is the brain behind Hadoop and coordinates all the distributed processing. Hadoop is written in Java, and writing MapReduce programs usually involves coding in Java. However, other languages like Python can be used as well. MapReduce has primarily two components; the Job Tracker and the Task Tracker. The Job Tracker services requests between you and the Hadoop ecosystem. It keeps track of the jobs processed, failures, etc., while to put it simply, a Task Tracker is to the Job Tracker what a Data Node is to a Name Node. In the Hadoop ecosystem, the NameNode services are parallel to storage, and the Job Tracker services are parallel to processing. MapReduce works in two phases:
- Map Phase: the map phase includes two tasks, Splitting and Reducing.
- Reduce Phase: The reduce phase includes two tasks, Shuffling and reduction
Yet Another Resource Negotiator (YARN) is a specific component of the open source Hadoop platform for Big Data analytics. In the first generation of Hadoop the MapReduce component executed job scheduling, resource management and job processing. This lead to limited scalability and resource utilization. Thus, to solve this problem, YARN emerged in between the HDFS and the MapReduce component as a cluster resource manager. This took care of resource management and job scheduling, leaving MapReduce to focus on job processing and achieving optimal resource utilization. Thus, the resource management functions and the job scheduling functions run on separate domains.
Apache Pig is a Hadoop ecosystem component, developed by Yahoo, and is used for processing unstructured data. Pig is an abstraction of MapReduce. Pig, analyses large data sets by representing them as the data flow. Additionally, to write scripts in Pig, we use Pig Latin, which is a high-level language, similar to SQL. MapReduce analyses large data sets in Hadoop. But MapReduce restricts to people who know Java. Writing a MapReduce task is easy in Pig Latin and is easy to learn if you are familiar with SQL.
To know more read our blog on ‘Apache Pig: A Beginner’s Guide’
Apache Sqoop is a general purpose tool to do a bulk transfer of data from databases like Oracle, MySQL to Hadoop. Moreover, Sqoop supports a number of traditional databases like Oracle, MySQL, Postgres, and Teradata. It also transfers data from Hadoop to traditional data stores. Transferring data from enterprise data store to a MapReduce application can be challenging if executed without Sqoop.
To know more read our blog on ‘Apache Sqoop: Beginner’s Guide’
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving high volume streaming data into the HDFS. Primarily, the Flume works as a logging system that gathers logs files from different machines and stores them in HDFS storage. It is robust with built-in reliability, fail over and recovery mechanisms.
To know more read our blog on ‘Apache Flume: Beginner’s Guide’
Anybody how wishes to work in the field of Big Data has to know the basics of Hadoop. Therefore, knowing these terminologies and their uses is a great place to begin with.