This article mainly explains about advantages and disadvantages of Hadoop. As the pillar of so many implementations, Hadoop is practically synonymous with big data. Offering dispersed storage, higher scalability, and ultimate performance, many people view this as the standard platform for high volume data infrastructures. To learn more about Hadoop, click on Hadoop Certification.
Advantages of Hadoop
The following are the advantages of Hadoop:
- Scalable: Hadoop is a highly scalable storage platform, because it can store and distribute large volume of data sets across hundreds of economical servers that perform in corresponding. Unlike traditional relational database systems (RDBMS) that can’t measure to route large amounts of data, Hadoop assists businesses to run applications on thousands of nodes involving thousands of terabytes of data.
- Cost effective: Hadoop allows businesses to simply access new data sources and rap into different types of data (both structured and unstructured) to generate value from that dataset. This means businesses can use Hadoop to develop valuable business visions from data sources such as social media, email conversations. Hadoop can be used for a wide range of purposes, such as log processing, recommendation systems, data warehousing, and market promotion analysis and fraud detection.
- Fast: Hadoop’s exclusive storage method is based on a distributed file system that basically ‘maps’ data anywhere it is located on a cluster. The tools for data processing are frequently on the same servers where the data is located, resulting in much faster data processing. If you are working with big sizes of unstructured data, Hadoop is able to capably process terabytes of data in just minutes, and petabytes in hours. To learn more about HDFS, click Big Data Hadoop Certification.
- Resilient Feature: Fault Tolerance is the significant advantage of using Hadoop. During failure, when data is sent to a specific node, data is replicated to other nodes in the cluster.
Here are the disadvantages of Hadoop namely:
- Security Concerns: Managing multifaceted applications such as Hadoop can be challenging. A simple example can be seen in the Hadoop security model, which is disabled by default due to absolute complexity. If whoever managing the platform lacks of know how to enable it, your data could be at huge risk. Hadoop is also missing encryption at the storage and network levels, which is a major selling point for government agencies and others that prefer to keep their data under wraps.
- Vulnerable by Nature: Speaking of security, Hadoop makes running it a hazardous suggestion. The framework is written almost entirely in Java, which is one of the most widely used but yet, the controversial programming languages in existence.
- Not Fit for Small Data: All big data platforms are not suited for small data needs whereas big data is not exclusively made for big businesses. Unfortunately, Hadoop is one of them. The Hadoop Distributed File System (HDFS) lacks the capacity to efficiently support the arbitrary evaluation of small files due to its high capacity design. As a result, it is not recommended for organizations with small quantities of data.
- Potential Stability Issues: Like all open source software, Hadoop has had its share of problems on stability issues. To avoid these issues, organizations are intensely endorsed to make sure they are running the latest stable version, or run it under a third-party vendor equipped to handle such problems.
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