Hadoop is an open-source framework used for big data processes. It’s humongous and has many components. Each one of those components performs a specific set of big data jobs. Hadoop’s vast collection of solutions has made it an industry staple. And if you want to become a big data expert, you must get familiar with all of its components.
Don’t worry, however, because, in this article, we’ll take a look at all those components:
What are the Hadoop Core Components?
Hadoop core components govern its performance and are you must learn about them before using other sections of its ecosystem. Hadoop’s ecosystem is vast and is filled with many tools. Another name for its core components is modules. There are primarily the following
Hadoop core components:
The full form of HDFS is the Hadoop Distributed File System. It’s the most critical component of Hadoop as it pertains to data storage. HDFS lets you store data in a network of distributed storage devices. It has its set of tools that let you read this stored data and analyze it accordingly. HDFS enables you to perform acquisitions of your data irrespective of your computers’ operating system. Read more about HDFS and it’s architecture.
As you don’t need to worry about the operating system, you can work with higher productivity because you wouldn’t have to modify your system every time you encounter a new operating system. HDFS is made up of the following components:
- Secondary NameNode
Name Node is also called ‘Master’ in HDFS. It stores the metadata of the slave nodes to keep track of data storage. It tells you what’s stored where. The master node also monitors the health of the slave nodes. It can assign tasks to data nodes, as well. Data nodes store the data. Data nodes are also called ‘Slave’ in HDFS.
Slave nodes respond to the master node’s request for health status and inform it of their situation. In case a slave node doesn’t respond to the health status request of the master node, the master node will report it dead and assign its task to another data node.
Apart from the name node and the slave nodes, there’s a third one, Secondary Name Node. It is a buffer to the master node. It updates the data to the FinalFS image when the master node isn’t active.
MapReduce is the second core component of Hadoop, and it can perform two tasks, Map and Reduce. Mapreduce is one of the top Hadoop tools that can make your big data journey easy. Mapping refers to reading the data present in a database and transferring it to a more accessible and functional format. Mapping enables the system to use the data for analysis by changing its form. Then comes Reduction, which is a mathematical function. It reduces the mapped data to a set of defined data for better analysis.
It pars the key and value pairs and reduces them to tuples for functionality. MapReduce helps with many tasks in Hadoop, such as sorting the data and filtering of the data. Its two components work together and assist in the preparation of data. MapReduce also handles the monitoring and scheduling of jobs.
It acts as the Computer node of the Hadoop ecosystem. Mainly, MapReduce takes care of breaking down a big data task into a group of small tasks. You can run MapReduce jobs efficiently as you can use a variety of programming languages with it. It allows you to use Python, C++, and even Java for writing its applications. It is fast and scalable, which is why it’s a vital component of the Hadoop ecosystem.
YARN stands for Yet Another Resource Negotiator. It handles resource management in Hadoop. Resource management is also a crucial task. That’s why YARN is one of the essential Hadoop components. It monitors and manages the workloads in Hadoop. YARN is highly scalable and agile. It offers you advanced solutions for cluster utilization, which is another significant advantage. Learn more about Hadoop YARN architecture.
YARN is made up of multiple components; the most important one among them is the Resource Manager. The resource manager provides flexible and generic frameworks to handle the resources in a Hadoop Cluster. Another name for the resource manager is Master. The node manager is another vital component in YARN.
It monitors the status of the app manager and the container in YARN. All data processing takes place in the container, and the app manager manages this process if the container requires more resources to perform its data processing tasks, the app manager requests for the same from the resource manager.
4. Hadoop Common
Apache has added many libraries and utilities in the Hadoop ecosystem you can use with its various modules. Hadoop Common enables a computer to join the Hadoop network without facing any problems of operating system compatibility or hardware. This component uses Java tools to let the platform store its data within the required system.
It gets the name Hadoop Common because it provides the system with standard functionality.
Hadoop Components According to Role
Now that we’ve taken a look at Hadoop core components, let’s start discussing its other parts. As we mentioned earlier, Hadoop has a vast collection of tools, so we’ve divided them according to their roles in the Hadoop ecosystem. Let’s get started:
Storage of Data
Zookeeper helps you manage the naming conventions, configuration, synchronization, and other pieces of information of the Hadoop clusters. It is the open-source centralized server of the ecosystem.
HCatalog stores data in the Binary format and handles Table Management in Hadoop. It enables users to use the data stored in the HIVE so they can use data processing tools for their tasks. It allows you to perform authentication based on Kerberos, and it helps in translating and interpreting the data.
We’ve already discussed HDFS. HDFS stands for Hadoop Distributed File System and handles data storage in Hadoop. It supports horizontal and vertical scalability. It is fault-tolerant and has a replication factor that keeps copies of data in case you lose any of it due to some error.
You’d use Spark for micro-batch processing in Hadoop. It can perform ETL and real-time data streaming. It is highly agile as it can support 80 high-level operators. It’s a cluster computing framework. Learn more about Apache spark applications.
This language-independent module lets you transform complex data into usable data for analysis. It performs mapping and reducing the data so you can perform a variety of operations on it, including sorting and filtering of the same. It allows you to perform data local processing as well.
Tez enables you to perform multiple MapReduce tasks at the same time. It is a data processing framework that helps you perform data processing and batch processing. It can plan reconfiguration and can help you make effective decisions regarding data flow. It’s perfect for resource management.
You’d use Impala in Hadoop clusters. It can join itself with Hive’s meta store and share the required information with it. It is easy to learn the SQL interface and can query big data without much effort.
The developer of this Hadoop component is Facebook. It uses HiveQL, which is quite similar to SQL and lets you perform data analysis, summarization, querying. Through indexing, Hive makes the task of data querying faster.
HBase uses HDFS for storing data. It’s a column focused database. It allows NoSQL databases to create huge tables that could have hundreds of thousands (or even millions) of columns and rows. You should use HBase if you need a read or write access to datasets. Facebook uses HBase to run its message platform.
Apache Drill lets you combine multiple data sets. It can support a variety of NoSQL databases, which is why it’s quite useful. It has high scalability, and it can easily help multitudes of users. It lets you perform all SQL-like analytics tasks with ease. It also has authentication solutions for maintaining end-to-end security within your system.
You can use Apache Sqoop to import data from external sources into Hadoop’s data storage, such as HDFS or HBase. You can use it to export data from Hadoop’s data storage to external data stores as well. Sqoop’s ability to transfer data parallelly reduces excessive loads on the resources and lets you import or export the data with high efficiency. You can use Sqoop for copying data as well.
Developed by Yahoo, Apache pig helps you with the analysis of large data sets. It uses its language, Pig Latin, for performing the required tasks smoothly and efficiently. You can parallelize the structure of Pig programs if you need to handle humongous data sets, which makes Pig an outstanding solution for data analysis. Utilize our apache pig tutorial to understand more.
Flume lets you collect vast quantities of data. It’s a data collection solution that sends the collected data to HDFS. It has three sections, which are channels, sources, and finally, sinks. Flume has agents who run the dataflow. The data present in this flow is called events. Twitter uses Flume for the streaming of its tweets.
Apache Kafka is a durable, fast, and scalable solution for distributed public messaging. LinkedIn is behind the development of this powerful tool. It maintains large feeds of messages within a topic. Many enterprises use Kafka for data streaming. MailChimp, Airbnb, Spotify, and FourSquare are some of the prominent users of this powerful tool.
Learn more – Hadoop Components
In this guide, we’ve tried to touch every Hadoop component briefly to make you familiar with it thoroughly. If you want to find out more about Hadoop components and its architecture, then we suggest heading onto our blog, which is full of useful data science articles.
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What are some features of Hadoop?
Hadoop is quite popular as a powerful Big Data tool. It is said to provide a highly reliable storage layer. Hadoop is an open-source project. It is highly scalable, which means that we can add any number of nodes to achieve high computation power. Fault tolerance is another important feature of Hadoop. HDFS uses a replication mechanism to provide fault tolerance. Hadoop provides high availability of the data even in unfavourable conditions and is cost-effective too. It stores data in a distributed fashion, which allows data to be processed distributedly on a cluster of nodes.
Why is MapReduce needed?
MapReduce is one of the components in the Apache Hadoop ecosystem. It is widely used for querying and selecting data in the Hadoop Distributed File System (HDFS). It is used for applications with iterative computation in which tonnes of data are to be parallely processed. The technology represents a data flow rather than a procedure. It is also suitable for large-scale graph analysis. MapReduce is also suitable for tasks like sorting, searching, indexing, classification, and joining.
What is Database Management, and where is it used?
A database management system is a computerised data-keeping system. Every user has access to different facilities of the system, so they can perform any range of operations that they wish to on the data, like defining, forming, managing, and monitoring all access to the database. It provides security and redundancy. It forms an insulating layer between programs and data abstraction. DBMS is used for the retrieval of information from the database, updating the database, and managing the database. A DBMS can grant access to various users and can determine how much of the data they can access from the database. Using Database management, the complex management of memory becomes easy to handle.