Advantages and disadvantages of Hadoop

Hadoop is a powerful tool for managing large volumes of information, offering significant benefits despite its drawbacks. Its ability to handle massive datasets makes it invaluable for organizations dealing with vast data. However, to effectively leverage its capabilities, it's essential to understand both its advantages and limitations.

Advantages of Hadoop 

1. A variety of resources for the data

The content, whether structured or disorganized, would be obtained from many resources through which inputs can be obtained, including messages, clickstream statistics, and even online networks. Every piece of content may need to be adapted to a uniform format, which is heavily time-consuming on your part. With the information from such a wide variety of sources, Hadoop is an extremely convenient tool. A few of its numerous features include the storing of information, the prevention of forgeries, and the assessment of various advertising strategies.

2. Efficient in terms of budget

Traditional methods forced companies to devote a significant percentage of their income to storing enormous amounts of information. In other cases, substantial portions of the original material had to be removed to make space for more recent data. So, there was a possibility that vital data might be lost. Hadoop was fully responsible for fully resolving this problem. It is a feasible and cost-effective choice to use for information archiving. It is preferable because it preserves all of the company's initial information. In the long term, information is easily accessible and may be referred to if the organization chooses to change the way its processes are carried out. If this had been carried out traditionally, the knowledge that had been acquired may have been lost due to the additional expenditures.

3. Speed

Every business uses some kind of network to speed up the process of getting things done. Because the company already uses Hadoop, its demands for digital warehousing could be satisfied by the technology. Within a decentralized network, the information is kept on a storage structure that is shared by all users. The activity of processing information may proceed more swiftly, given that the tools necessary to handle the information are located on the same systems as the information itself. Hadoop makes it possible to process terabytes of data in seconds rather than hours.

4. Numerous versions

Hadoop immediately makes many copies of the information kept inside it. This guarantees that no information is lost when something goes wrong. Hadoop recognizes that the information is important and must not be missed unless the company decides to destroy it.

5. Abstraction

Encapsulation may be provided by Hadoop on several levels of the processing. As a consequence of this, the job of the programmers has been made easier. A huge document is often split into smaller files known as blocks. Each block retains the same dimensions and is stored in its own section of the larger group. When we are constructing the map-reduce task, we need to consider the location of the blocks. We offer the whole text as the data, and the Hadoop platform is responsible for doing analytics on the individual information blocks, which may be stored in many different locations. The Hadoop platform is the foundation for the Hive abstraction, developed above it. It is a part of the Hadoop cluster that you may use. Because MapReduce jobs are built in Java, SQL programmers all over the globe were unable to utilize MapReduce. 

6. Data Locality

Data Locality is a concept in Hadoop that refers to the fact that information is stored statically and that code is moved to the location in tasks. Because moving petabytes of data across the system is difficult and expensive, the cluster's information must remain as localized as possible. This ensures that the cluster's information transmission is kept to a minimum.

Disadvantages of Hadoop 

1. Latency

The MapReduce framework in Hadoop is notably slower than other system components since it must accept a broad range of information kinds and formats in addition to a vast volume of information. Hadoop was designed to process enormous amounts of data. The "Map" component of MapReduce takes one set of information and decodes it into "an entirely another sample of information," in which the separate parts are broken down into "key-value pairs." In general, "Reduce" takes the output from the map as insight and processes it further. On the other hand, "MapReduce" needs a lot of time to execute these activities, which increases "latency."

2. Failure to Take Necessary Precautions

When a corporation handles sensitive data that it has obtained, it is required to implement the appropriate precautions for data security. In Hadoop, the safety precautions are deactivated by default. The person in charge of data analytics has to be aware of this to ensure the data's safety.

3. Problems with small data

Even though there are many large-scale systems, some are not suited for working on smaller scales. Hadoop is an excellent example of a system that might be used only by large corporations with a lot of information since it can store a lot of data. It is inefficient in situations when there is little information.

Hadoop's scope does not allow for the consideration of information of a minor nature. Due to the enormous volume design of the distributed file system used by Hadoop, it is impossible to perform the generic processing of small documents in an efficient manner.

HDFS is experiencing significant difficulties due to the lack of data. HDFS has a block volume that is far lower than the file capacity of even the smallest document (default 128MB). Because HDFS is intended to deal with a restricted set of large documents for keeping vast amounts of information kinds, attempting to use HDFS to store a major proportion of small folders will not work. HDFS was meant to deal with large documents. If there are many very small files, the NameNode, which is responsible for storing the name of HDFS, will get overwhelmed. 

4. Functioning in a Dangerous Way

The programming language that is now in the most widespread usage is Java. Java has been brought up in a number of different discussions recently due to the simplicity with which cybercriminals may exploit systems that are based on Java. Hadoop is one example of a platform that is built on Java. As a direct consequence of this, the system is susceptible to assault, which may have negative consequences. 

Conclusion

Hadoop offers significant advantages in managing and processing large datasets, making it an invaluable tool for organizations dealing with vast amounts of data. Its ability to efficiently store and process data from diverse sources, cost-effective solutions, and enhanced processing speed make it a go-to choice for many. However, Hadoop also has its drawbacks, including latency issues, security vulnerabilities, and inefficiencies with small datasets. Understanding these pros and cons is crucial for organizations to optimize their data management strategies. As data processing needs evolve, tools like Spark and Flink can complement Hadoop, enhancing its capabilities and addressing its limitations. For those looking to master Hadoop and other data technologies, For those looking to master Hadoop and other data technologies, Simpliaxis offers comprehensive courses in Big Data analytics training and Hadoop, providing the skills needed to stay ahead in the field.