Hadoop is an open-source software framework for storing huge volumes of data. It gives the users a massive data storage facility, enormous computational power, and the ability to handle several virtually limitless jobs or tasks. Basically, its main core component is to support growing big data technologies. With Hadoop, you can handle multiple modes of data such as structured, unstructured, and semi-structured data. It allows you the flexibility to collect, process, and analyze data which the older data warehouses failed to do. If you want to grow in the field of Hadoop framework, Big Data Hadoop Online Training can help you in the process.
What is Hadoop Ecosystem
The Hadoop ecosystem is a platform that aids in solving big data problems. It comprises different components and services. Most of the services available in the Hadoop ecosystem are basically to supplement the main four core components of Hadoop which include HDFS, YARN, MapReduce, and Common. The Hadoop ecosystem includes Apache Open Source projects and other varieties of commercial tools and solutions.
The components of the Hadoop Ecosystem
HDFS:
HDFS is the major component of the Hadoop ecosystem and is responsible for storing large data sets. It stores, both structured and unstructured data across various nodes and thereby maintain the metadata in the form of log files.
HDFS comprises two core components
Name node
Data Node
The name Node is the prime node that contains metadata requiring comparatively fewer resources than the data nodes that store the actual data. Thereby, making Hadoop cost-effective. However, HDFS maintains all the coordination between the clusters and hardware.
YARN:
YARN helps to manage the resources across the clusters. Typically, it performs scheduling and resource allocation for the Hadoop System.
It comprises three major components i.e.
Resource Manager
Nodes Manager
Application Manager
The resource manager provides resources for the applications in a system. While Node managers work on the allocation of resources such as CPU, memory, and bandwidth per machine and later on acknowledge the resource manager. Finally, the Application manager works as an interface between the resource manager and node manager and thus performs negotiations as per the requirement of the two.
MapReduce:
MapReduce carries over the processing’s logic and helps to write applications that transform big data sets into a manageable ones. It basically makes use of two functions, Map() and Reduces () whose task is: Map() performs sorting and filtering of data and further organizes them in the form of groups. Map generates a key-value pair-based result which is next processed by the Reduce() method.
PIG:
The pig was developed by Yahoo which works on a pig Latin language. It provides a platform for structuring the data flow, processing, and analyzing huge data sets. Pig basically does the work of executing commands and in the background, all the activities of MapReduce are taken care of. After the processing, the pig stores the final result in HDFS. In addition, Pig helps to achieve ease of programming and optimization.
HIVE:
Taking help from SQL methodology and interface, HIVE performs the reading and writing of large data sets. Though, it is highly scalable and allows real-time processing and batch processing. Also, all the SQL datatypes are supported by Hive thereby, making the query processing easier.
Mahout:
Mahout enables Machine Learnability to a system or application. It enables various libraries or functionalities such as collaborative filtering, classification, and clustering. It even allows invoking algorithms as per your needs with the help of its own libraries.
Apache Spark:
It’s a platform that looks after the process of consumptive tasks like batch processing, interactive or iterative real-time processing, visualization, graph conversions, etc. It also consumes memory resources; thus it is faster than the prior in terms of optimization. Spark better suits real-time data whereas Hadoop is best suited for structured data or batch processing. Thus, both are majorly in use by companies interchangeably.
Apache HBase:
It’s a NoSQL database that supports all kinds of data and is hence capable of handling anything in a Hadoop Database. At times, when you need to search or retrieve the occurrences of something small in a huge database, the request can be processed within a short quick span of time.
CONCLUSION
After discussing each component of Hadoop, you have got an idea that each component exhibits a certain function. To become an expert in Hadoop, you can take Big Data Hadoop Training in Delhi for easy and fast learning. So, start your journey with learning and excel in the field of the Hadoop ecosystem.
