Hadoop is a blessing for organizations forced to deal with humongous quantities of data. Thanks to this open-source software, they receive a framework for managing both structured and unstructured information. Hadoop helps to store the data in diverse locations, process it simultaneously, and analyze. This should make you feel that this tool can only prove advantageous to all. However, like everything else, it has its fair share of disadvantages too, despite the many benefits that it awards to its users.
Pros of Using Hadoop
Open-Source Software
The source code of this software is freely available. Therefore, if ever you need to, you may modify this code to make it suitable for a particular requirement.
Good Compatibility
This software can get along with all types of data! The information may arrive through email exchanges, social media posts/chats, etc. Hadoop believes that every bit of data holds some value, regardless of whether it is in the unstructured/structured format. Therefore, CSV files, XML files, text files, images, etc. all are welcome.
Hadoop is equally friendly with the emerging technologies connected with Big Data. To illustrate, Flink, Spark, etc. have processing engines, which utilize this software as a platform for storing data.
Support for Multiple Languages
The framework supports several languages, such as Groovy, C, C++, Ruby, Perl, and Python. Therefore, developers find the coding process quite easy.
User-Friendly
It helps that processing of data co-occurs, as diverse bits of data move to different locations. In other words, distributed production takes place automatically at the backend. This frees programmers from worrying about it!
Healthy Throughput
Throughput refers to the completion of a task per unit time. Hadoop divides every big task into smaller ones. This enables users to work on diverse chunks of data simultaneously. Therefore, the throughput is high.
Low Network Traffic
Sub-tasks assign themselves to various data nodes. Therefore, the user can move small quantities of code to diverse data sets. Since huge amounts of data are not moving at once, network traffic remains low.
Speedy in Performance
The architecture of the framework is unique. Therefore, both distributed storage and distributed processing of huge quantities of data take place at high speed. For instance, it creates several blocks for storing data. Then, it stores the information flowing into these blocks. These blocks stretch over multiple nodes.
Similarly, whenever the user submits a specific task, Hadoop divides it into various sub-tasks. Each sub-task reaches a particular worker node, where requisite data is already present. Thus, distribution and processing take place in parallel, thereby enhancing performance.
Horizontally Scalable
This is the principle that governs Hadoop. You just need to bring the cluster of nodes and the entire machine together in the framework. It is possible to do this while on the go or even when in a hurry. Hadoop is not vertically scalable. Otherwise, you would have to alter the configuration, such as adding disk, RAM, etc.
Healthy Availability
Hadoop 2.x has a reliable architecture. This HDFS architecture possesses two NameNodes. One is active, while the other is a standby in case something goes wrong. However, Hadoop 3.0 is an improvement on the previous version. It has multiple NameNode standbys in place. Therefore, you need not worry about sudden crashes.
Fault Tolerance
Erasure coding is the fault-tolerant tool, which is available for Hadoop 3.0. Even if a node fails, it is still possible to recover the concerned data block. Other blocks, as well as special parity blocks, assist in this task.
Cost-Effectiveness
Commodity hardware comes into play for storing data. Commodity hardware refers to inexpensive machines. Therefore, adding nodes to Hadoop is affordable too. Thus, this software would be an economical add-on to computer systems.
Cons of Using Hadoop
Inability to Handle Small Files
Hadoop can handle large files easily, albeit if they are in a small number. The small file is around 128/256 MB, smaller than the size of a Hadoop data block. However, it tends to face difficulty in tackling a large number of small files. They overload the NameNodes. NameNodes already have the job of storing namespaces.
Incurs Processing Overhead
The entire reading and writing operations take place on the disk. In other words, the framework garners data from the disk and inputs them on the disk. Therefore, Hadoop can prove expensive to use, specifically if you are handling terabytes and petabytes of data. Hadoop fails at tackling in-memory calculations, thereby incurring process overhead.
Bonds only with Batch Processing
Hadoop’s batch-processing engine proves inefficacious for stream processing. Low latency prevents real-time output. The drawback is that Hadoop requires garnering and storing of data in advance, for effective functioning.
Unable to Deal with Machine Learning
Machine learning is iterative processing; wherein there is a cyclic data flow. Hadoop is used to the inflow of data in stages. For instance, the output of stage one will become the input of stage two.
Faces Security Risks
Hadoop favors a highly popular, global programming language – Java. Therefore, cybercriminals find it easy to exploit Hadoop. Security breaches remain a genuine problem.
Similarly, Hadoop requests Kerberos authentication. It is not easy to manage this kind of authentication. For instance, there is no encryption coming into play while storing data. The same thing happens at the network levels too. Naturally, users and organizations feel worried about the safety of using Hadoop. The software is vulnerable to online thieves.
Hadoop has its disadvantages; however, the advantages far outweigh them. Therefore, it continues to gain in popularity across the IT arena. Organizations are on the lookout for people with expertise in handling Hadoop. Consequently, you might like to obtain a Hadoop Certification, especially if you desire a lucrative career boost. However, you would do well to link up with a genuine coaching establishment with professional trainers at the ready.
Laila Azzahra is a professional writer and blogger that loves to write about technology, business, entertainment, science, and health.