With the rapid change in technology, the world is becoming more and more information-driven. By a gauge, around 90% of the world’s database has been created over the past two years alone. In addition, 80% of the information is unstructured and accessible in a greatly varying structure making the process of its analysis next to impossible.
Moreover, the problem does not end here; along with the enormous amount of data, the most significant challenge arises with the fact that this data is in no particular arrangement or format. It has text, pictures, videos, audios, GPS tracking details, Sensor Records and what not. Briefly, its unstructured data! The conventional systems work great with limited, structured datasets, but cease to perform with a colossal amount of unstructured data and that’s when Hadoop comes into the picture.
Hadoop offers a solution to process, handle and combine extremely large sets of unstructured, structured and semi-structured data or Big Data to be more precise. Hadoop is the solution to Big Data. Hadoop aims at leveraging the possibilities provided by Big Data and overcomes the challenges that it may encounter. It is an open-source, Java-based programming framework that manages the processing of large data sets in a distributed or clustered environment. The tiny elephant in the big data room has now become the most popular big data solution across the globe.
A fair Hadoop Architecture required diverse design considerations in terms of networking, computing power, and storage. It follows a Master-Slave Architecture for the handling and analysis of large datasets. The following factors need to be considered while building and designing a Hadoop Cluster:
1. Hadoop Distributed File System (HDFS)
The HDFS is the primary data storage system designed to run on commodity hardware. It is a distributed file system than can easily run on clusters to process Big Data. It runs few applications on distributed systems with thousands of nodes involving petabytes of information. Due to this feature of HDFS, it is highly robust and fault-tolerant. It stores data in multiple locations therefore in the case of failure of any storage location, the data can be recovered from another location ensuring data safety. It employs a NameNode and DataNode structure to execute a distributed file system that provides very efficient and speedy access to data across highly scalable Hadoop structures.
2. Hadoop MapReduce
The conventional systems used to use a centralized server for retrieving and storing data which was fine as long as the data was structured and small, but with the exponential increase in data, its accommodation was a big problem for the standard database servers. This problem tickled google first due to their search engine data, which exploded with data due to the revolution of the internet industry. They smartly resolved this difficulty using the theory of parallel processing and designed an algorithm called MapReduce. It distributes the task into small pieces and assigns those pieces to many machines joined over a network, and assembles all the events to form the last event dataset. The fundamental unit of information used by MapReduce is a key-value pair. All the data, whether structured or not, needs to be translated to the key-value pair before it is passed through the MapReduce model. In the MapReduce Framework, the processing unit is mover to the data rather than moving the data to the processing unit.
Apache Hadoop YARN is the job scheduling and resource management technology in the open-source Hadoop distributed processing framework. Yarn allows different data processing engines like stream processing, interactive processing, as well as batch processing to process and execute data stored in the HDFS (Hadoop Distributed File System). It is one of the core components of Hadoop that extends the power of Hadoop to other evolving technologies so that they can be benefitted from the HDFS which is the most renowned and popular storage system of the contemporary tech-world. Apart from resource management, YARN is a data operating system for Hadoop 2.x and also does job scheduling. It facilitates Hadoop to process other purpose-built data processing systems other than the MapReduce. It allows the processing of several frameworks on the same hardware where Hadoop is deployed.
Despite being thoroughly proficient at data processing and computations, Hadoop had a few shortcomings like scalability issues, delays in batch processing, etc. as it relied on MapReduce for processing big datasets. With YARN, Hadoop is now able to aid a diversity of processing approaches and has a greater array of applications. Hadoop YARN clusters are now able to stream interactive querying and run data processing side by side with MapReduce batch jobs. YARN framework runs even on the non-MapReduce applications, thus overcoming the shortcomings of Hadoop 1.0. Advantages of Hadoop
- Scalable: Hadoop is a profoundly scalable storage platform since it can store and disperse extremely datasets across several economical servers that work in parallel. In contrast to conventional relational database systems (RDBMS) that can’t scale to process a lot of data.
- Cost-Effective: Hadoop likewise offers a very cost-effective storage solution for organizations detonating on datasets. The issue with Relational database management systems is that it is incredibly cost restrictive to scale to such an extent so as to process such huge volumes of data. With an end goal to diminish costs, numerous organizations in the past would have needed to down-sample data and group it depending on certain assumptions regarding which data was the most significant. The raw data would be erased, as it would be too cost-restrictive to keep.
- Adaptable: Hadoop empowers organizations to effortlessly access new data sources and tap into various kinds of data (both structured and unstructured) to create an incentive from that data. This implies organizations can utilize Hadoop to get profitable business stats from data, for example, social media, email-conversations, etc.
- Quick: Hadoop’s one of a storage method depends on a distributed file system that fundamentally ‘maps’ data wherever it is situated on a cluster The tools for data processing are often on the same servers where the data is located, resulting in a much faster data processing. In case you’re managing enormous volumes of unstructured data, Hadoop can proficiently process terabytes of information in just minutes.
- Resilient to failure: A major advantage of using Hadoop is its adaptation to internal failure and fault tolerance. When data is sent to an individual node, that data is also replicated to other nodes in the cluster, which means that in the event of failure, there is another copy available for use.
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