Hadoop – Main advantages and disadvantages
Big Data is today one of the main areas of intervention in the digital world. Tons of data are generated and collected from the various business processes. This data can include models and methods to improve business operations. The data also provides customer feedback. Of course, this data is essential for the business and should not be deleted. However, the full set is also not valid. Some data is unnecessary. This set should distinguish and eliminate the precious piece.
Various platforms are used to carry out this vital process, and Hadoop is the most popular platform. Hadoop can efficiently analyze and obtain useful information. It also has its range of advantages and disadvantages.
What is Hadoop?
Hadoop is designed for large amounts of data storage and management. Hadoop has many advantages, such as being free and open-source, easy to use, and powerful, apart from a few disadvantages.
Doug Cutting and Michale J developed Hadoop. It is administered by an Apache software foundation and licensed under the Apache 2.0 Hadoop license. It is advantageous for large companies because it relies on low cost servers, which needlessly store data and process it. By providing a history of data and various documents of the company, Hadoop helps to make a better business decision.
So a business can improve its business by using this technology. Hadoop processes in depth the data collected from the company to deduce the result that can contribute to a future decision. These platforms serve an essential purpose for the business.
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Let’s start by exploring the main advantages and disadvantages of Hadoop.
Main advantages of Hadoop
- Various data sources: Hadoop accepts a wide range of data. Data can come from a variety of sources including email conversations, social media, and a structured or unstructured form. Hadoop can derive the value of multiple data in a text file, XML, images, and CSV file.
- Profitable: Hadoop is a cost effective solution because it uses a hardware cluster to store the data. The basic material is a cheap machine, so the knots in the frame are not very expensive. Redundant data has dramatically decreased and requires fewer machines to store data.
- Flexible: Hadoop enables organizations to quickly access new data sources and leverage various types of data (structured and unstructured) to generate value from that data. In other words, businesses can use Hadoop from data sources, for example, social media, email conversations, or data escalation, to gain valuable business insight. Additionally, you can use Hadoop for a variety of purposes including log processing, recommendation systems, data storage, market campaign analysis, and fraud detection.
- Speed: Every business uses a platform to do their jobs faster. The data is stored in a file system distributed through a storage system. Since the tools used for data processing are located on the same servers as the data, the processing operation is also accelerated. So in minutes, Hadoop developers can process terabytes of data.
- Minimum network traffic: Hadoop divides each task into several smaller jobs assigned to each available data node within 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 creates multiple copies of the data stored there. It is done to ensure that information is not lost in the event of a failure. Data is essential and should be retained if the company deletes it.
- Broadband: Throughput refers to the task performed per unit of time – Hadoop stores data in a distributed form that allows easy processing of distributed data. A specific job is divided into smaller jobs that work on parallel data items, resulting in a high level of performance.
- Scalability: Hadoop is a very scalable model. In a cluster processed in parallel, large amounts of data are split into multiple machines at low cost. The number of such devices or nodes can be increased or reduced depending on the needs of the business. You cannot scale systems to process large amounts of data under traditional Relational DataBase Management System (RDBMS).
- Problem with small files: Hadoop can run efficiently on a small number of large files. Hadoop saves the file as blocks from 128MB (default) to 256MB in size. Hadoop fails when you need to access a large amount of the small file size. So many small files add the Namenode and make the job difficult.
- Vulnerability: Hadoop is a framework written in Java. Java is one of the most widely used programming languages which makes it more dangerous because it is easy to use by any cyber criminal.
- Low efficiency in the small data environment: Hadoop is primarily designed to handle large data sets. Businesses that generate massive amounts of data can use it effectively. This decreases efficiency while operating in a small data environment.
- Risky operation: Java was also linked to various controversies, as cyber criminals can easily exploit frameworks built by Java. The platform is therefore vulnerable and can cause unpredictable damage.
- Treatment of overheads: Data is read from disk and written to disk, making read / write very expensive for tera and petabytes data. Hadoop cannot compute in memory and therefore supports processing.
Software in each industry has its own set of disadvantages and advantages. If software is vital to the organization, you can reap the benefits and minimize the flaws. Big Data has been needed to collect information and find hidden facts behind the data with the growth of the industry. Data defines how businesses can improve marketing and business.
A wide variety of industries revolve around data. There is a lot of data collected and analyzed through different processes with different tools. Hadoop is one of our tools for handling this vast 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 the demands of big data.
Posted on May 23, 2021