Hadoop – Top Pros and Cons


Big data is one of the main focuses in the digital world today. Tons of data are generated and collected from the various company processes. This data may include patterns and methods used to improve the company’s business. The data also provides customer feedback. Of course, this data is essential to the business and should not be discarded. However, the entire sentence is also not valid. Some data is useless. This sentence should distinguish and discard the valuable part.

Various platforms are used to perform this important process, and Hadoop is the most popular one. Hadoop can analyze and obtain helpful information efficiently. It also has a number of advantages and disadvantages.

What is Hadoop

Hadoop was designed for large amounts of data storage and management. Hadoop has many advantages, such as: B. free and open source features, ease of use and performance, apart from a few drawbacks.

Doug Cutting and Michale J developed Hadoop. It is administered by an Apache software foundation and licensed under the Apache License 2.0 Hadoop. This is beneficial for large companies as it is based on inexpensive servers that store and process data unnecessarily. By providing a data history and various corporate documents, Hadoop helps make better business decision.

This is how a company can use this technology to improve its business. Hadoop processes the data collected by the company extensively to derive the result that can contribute to a future decision. These platforms serve an essential purpose for the company.

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Let’s start with the main advantages and disadvantages of Hadoop.

Top Hadoop benefits

  • Diverse data sources: Hadoop accepts a wide variety of data. Data can come from a variety of sources including email conversations, social media, and structured or unstructured forms. Hadoop can infer the value from multiple pieces of data in a text file, XML, images, and CSV files.
  • Cost efficient: Hadoop is a cost effective solution because it uses a hardware cluster to store data. Commodity hardware is a cheap machine, so nodes in the framework aren’t very expensive. The redundant data has decreased significantly and requires fewer machines to store data.
  • Flexible: With Hadoop, companies can quickly access new data sources and use different types of data (structured and unstructured) to get value from this data. In other words, companies can use Hadoop from data sources such as social media, email conversations, or climbing data to generate valuable business insights. In addition, you can use Hadoop for a variety of purposes including log processing, recommendation systems, data storage, market campaign analysis, and fraud checking.
  • Speed: Every company uses a platform to get their work done faster. The data is stored in a distributed file system via a storage system. Since the tools used for data processing are on the same servers as the data, the processing process is also accelerated. This enables Hadoop developers to process terabytes of data in minutes.
  • Minimum network traffic: Hadoop divides each task into several small jobs that are assigned to each available data node in the Hadoop cluster. A small amount of data is processed in each data node, resulting in low traffic within a Hadoop cluster.
  • Multiple copies: Hadoop duplicates and makes multiple copies of the data stored there. It is ensured that no information is lost in the event of an error. The data is essential and should be retained if the company discards it.
  • High throughput: The throughput relates to the task completed per unit of time. Hadoop stores data in a distributed form that enables easy processing of distributed data. A particular job is divided into small jobs that work with parallel data and achieve a high level of performance.
  • Scalability: Hadoop is a very scalable model. In a cluster that is processed in parallel, large amounts of data are divided into several inexpensive machines. The number of such devices or nodes can be increased or decreased depending on the needs of the business. You cannot scale the systems under the traditional RDBMS (Relational DataBase Management System) to handle large amounts of data.


  • Problem with small files: Hadoop can handle a small number of large files efficiently. Hadoop saves the file as blocks ranging in size from 128MB (default) to 256MB. Hadoop fails when you need to access a large amount of the small file. So many small files add the naming code and make the job difficult.
  • Vulnerability: Hadoop is a framework written by Java. Java is one of the most widely used programming languages, which makes it more unsafe as it can be easily used by any cyber criminal.
  • Low efficiency in the environment of small amounts of data: Hadoop was developed primarily to process large amounts of data. The companies that generate a huge volume of data can use it efficiently. It reduces the efficiency of execution in a small data environment.
  • Risky functioning: Java has also been associated with various controversies as cyber criminals can easily take advantage of frameworks created by Java. The platform is therefore fragile and can lead to unpredictable damage.
  • Overhead processing: The data is read from the disc and written to the disc, making it very expensive to read / write for tera data and petabytes. Hadoop cannot compute in memory, so it requires overhead processing.

last words

Each industry’s software has its own drawbacks and advantages. When software is critical to the business, you can reap the benefits and minimize errors. Big data was needed to gather information and find hidden facts behind the data as the industry grew. Data defines how companies can improve marketing and business.

A wide range of industries revolve around the data. There is a lot of data that is collected and analyzed through different processes with different tools. Hadoop is one of our tools to handle this large amount of data because it can easily extract information from data. We see that Hadoop has advantages in overcoming its weaknesses and is a powerful solution to big data needs.

Published on May 23, 2021


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