Data is rightly referred to as the ‘digital gold’ of modern times. Across all major industries and scales of operation, businesses require seamless data management to carry out their processes and make sense of the same. In the age of digitization, the relevance of big data has increased significantly. Organizations today need to store, process, track, and analyze millions of records to make important business decisions and increase the efficiency of processes.
When it comes to processing big data, Hadoop is often the go-to option for most organizations. The open-source software framework allows users to store and process big data with ease and efficiency. It stores large volumes of data on inexpensive commodity servers running as clusters. Hadoop adopts a distributed file system that facilitates fault tolerance and concurrent processing of data.
Hadoop is known for its massive storage capacity for all kinds of data. It has the ability to handle virtually endless jobs and concurrent tasks. Due to the high demand for big data processing, organizations around the world spend a good amount of time comparing all available alternatives in the market, the most common being the Hadoop vs Spark comparison. However, Hadoop remains one of the most preferred frameworks when it comes to storing and processing big data.
Here are some of the most noteworthy reasons why Hadoop is ideal for big data:
When you are dealing with big data, you may need to make changes in the scale of your operations at any time. For this, you need a software framework that allows you to bring about smooth and sustainable changes in your scale.
Hadoop is a highly scalable framework due to its capacity of storing, distributing, and processing considerably large volumes of data across several servers operating in parallel. This is one of the attributes that set Hadoop apart from the conventional Relational Database Systems (RDBMS) that are not capable of scaling up or down based on the users’ requirements. With Hadoop, you can adjust the scalability of your business while running applications using thousands of nodes handling thousands of terabytes worth of data.
Another major benefit of using Hadoop for big data is that it facilitates cost-effective storage and processing of data. When you are dealing with considerably high volumes of data, there is always a risk of going overboard with the expenses. However, storing and processing big data using Hadoop helps you evade this problem.
Hadoop provides users with a cost-effective solution for storing, processing, and analyzing millions of records without compromising the quality of services. One of the biggest issues with conventional relational database management has always been of it being cost-prohibitive to scale when it comes to handling big data. Hadoop resolves this issue by offering solutions that are affordable for most businesses.
Hadoop prevents organizations from down-sampling their data and making assumptions about which datasets are most valuable to them. It also prevents users from deleting raw data which might have been useful just to save the cost of data storage and processing. Being designed as a scale-out architecture, Hadoop allows companies to store all their data for later use without spending a fortune behind the same.
While managing big data, you cannot afford to lose time, effort, and money in making changes within your system and processes. Hadoop provides users with the much-needed flexibility that allows them to access new sources of data with the utmost ease.
Moreover, Hadoop allows users to tap into both structured and unstructured datasets based on their specific needs and preferences to generate value from the same. Based on your requirements, you can use Hadoop for obtaining valuable insights from sources like email conversations, social media records, clickstream data, and other sources that would provide you with large data volumes.
Along with this, Hadoop can be used for big data processing to serve a range of different purposes such as managing recommendation systems, processing logs, data warehousing, analyzing campaigns, detecting frauds, and much more. The software framework does not restrict users from getting a range of different tasks completed and getting the results they are looking for.
If you are using a conventional software framework with a lower speed for processing big data, it would affect the speed at which your business processes are carried out. In the age of digitization where reports and analytics demand being carried out within a few seconds, Hadoop makes sure that you and your team members do not waste your time processing big data.
The unique storage method offered by Hadoop and the distributed file system it is based on allows users to map their data irrespective of the location where it is located on a cluster. Here, the tools offered by the framework for data processing are often present on the same servers where your data is located. This makes data processing much faster and more efficient.
High Fault Tolerance
Hadoop sports a fairly high resilience to failure. When you send your data to an individual node, the same is replicated in other nodes within the cluster. This way, you will always have another copy available for use in the event of a failure.
Moreover, the MapR distribution in Hadoop eliminates the NameNode and replaces the same with a distributed No NameNode architecture. This architecture provides users with true high availability, protecting the system from single as well as multiple failures.
The tendency of Hadoop to deal with failures and maintain its resilience prompts businesses operating at all scales to implement the solution for managing big data, especially for making sense of unstructured datasets.
The Final Word
These were some of the most important reasons why Hadoop is used for storing, managing, and processing big data. Looking at the speed at which businesses are driving digitization, it is safe to say that the prominence of big data management and analytics would only increase in the years to come. It is therefore advisable for organizations to implement software frameworks like Hadoop to become future-proof and stay in sync with the ongoing tech trends.