Big data is one of the main focus areas in the digital world today. Tons of data are generated and collected from the different company’s processes. This data may include patterns and methods for improving the company’s operations. The data also provides customer feedback. Of course, this data is essential to the company and should not be discarded. However, the whole set is also not valid. Some data is useless. This set should distinguish and discard the valuable part.
Various platforms are used to carry out this vital process, and Hadoop is the most popular platform. Hadoop can analyze and obtain helpful information efficiently. 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 benefits, such as being free and open-source, easy to use, and performance 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 license 2.0 Hadoop. It is advantageous for big businesses because it is based on low-cost servers, which needlessly store data and process data. By providing a history of data and different company documents, Hadoop helps make a better business decision.
Thus a company can improve its business by using this technology. Hadoop processes the data collected from the company extensively to deduce the result that can contribute to a future decision. These platforms serve an essential purpose for the enterprise.
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Let’s begin to explore Hadoop’s top advantages and disadvantages.
Top Hadoop Advantages
- Varied data sources: Hadoop accepts a wide range of data. Data may come from various sources, including Email conversations, social media, and structured or unstructured form. Hadoop can derive the value from multiple data in a text file, XML, images, and CSV file.
- Cost-effective: Hadoop is a cost-effective solution since it uses a hardware cluster to store data. Commodity hardware is cheap machinery, so nodes in the framework are not very expensive. The redundant data has significantly decreased and requires fewer machines to save data.
- Flexible: Hadoop allows firms to access new data sources quickly and tap into various (structured and unstructured) data types to produce value from those data. In other words, companies may take Hadoop from data sources, for example, social media, email conversations, or climbing data, to gain valuable business insights. Furthermore, you can use Hadoop for various purposes, including log processing, recommendation systems, data storage, market campaign analysis, and screening for fraud.
- Speed: Each company uses a platform to do its jobs more quickly. The data is stored in a distributed file system via a storage system. Because the tools used for data processing are located on the same servers with the data, the processing operation is also accelerated. Thus in minutes, Hadoop developers can process terabytes of data.
- Minimum network traffic: Hadoop divides each task into several small jobs assigned to each available data node within the Hadoop cluster. A small number of data is processed in every data node leading to low traffic within a Hadoop cluster.
- Multiple copies: Hadoop duplicates and creates several copies of the data that is stored there. It is done to ensure that information is not lost in the event of a failure. The data is essential and should be maintained if the company discards it.
- High throughput: Throughput refers to the task done per unit time—Hadoop stores data in a distributed form that enables easy processing of distributed data. A specific job is divided into small jobs that work on parallel pieces of data, which yield a high level of performance.
- Scalability: Hadoop is a very scalable model. In a parallel processed cluster, large quantities of data are divided into several low-cost machines. The number of such devices or nodes can be increased or reduced according to the company’s requirements. You cannot scale up the systems to deal with large amounts of data under traditional RDBMS (Relational DataBase Management System).
- Problem with small files: Hadoop can perform 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 have to access a large amount of the small size file. So many small files add the Namenode and make it hard to work.
- Vulnerability: Hadoop is a Java-written framework. Java is amongst the most used programming languages, making it more unsafe because it is easy to use by any cyber-criminal.
- Low efficiency in the surroundings of small data: Hadoop is mainly designed to handle large data sets. The companies that generate a massive volume of data can use it efficiently. It decreases efficiency while performing in a small data environment.
- Risky functioning: Java was also linked to different controversies since cybercriminals can easily exploit Java-built frameworks. The platform is therefore vulnerable and may result in unpredictable damage.
- Overhead processing: The data is read from the disc and written to the disc that makes reading/writing very expensive for tera data and petabytes. Hadoop cannot calculate in memory, and therefore it takes overhead processing.
Each industry’s software has its own set of disadvantages and advantages. If the software is vital to the organization, you can take advantage of the benefits and minimize defects. Big Data has been needed to gather information and find behind the data hidden facts with the industry growing. Data defines how enterprises can enhance marketing and business.
A wide range of industries revolves around the data. There are numerous data collected and analyzed through different processes with different tools. Hadoop is one of our tools to handle 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 Big Data requirements.