How Can Hadoop Solve Your Big Data Problems at Once?

Do you know more than 2.5 quintillion bytes of data is generated every day, and by an estimate, nearly 90.0% of the world’s data has been created during 2018-2019? 

                                                                                                                                                    The global big data market is predicted to surge to $103 billion by 2027, with an increasing CAGR of 45.0%.  

Due to the surging mobile data traffic, burgeoning adoption, and development of technologies, such as AI and IoT, the complexity and volume of data are immensely increasing and boosting the growth of the big data market. 

Further, 80.0% of the data is available in various structures and unstructured formats, which are quite difficult to analyze. 

The amount of data produced in a day is huge, and such a significant amount of data brings a plethora of challenges, including the problems of arranging data in a fixed format that needs to be resolved with traditional or modern systems. 

There is no doubt, traditional systems have proved to be very useful in structuring data, but unfortunately, they can’t manage a huge amount of unstructured data or what we call big data.  

But, what is the need for storing this big data and processing it? 

Well, the answer is you need this data to make calculative and better decisions in the field you are working in. Usually, these decisions are related to the prevention of fraud activity or guessing the preferences of consumers. 

So, how to manage these data in a perfect way? By deploying Hadoop!

Or you can hire big data application development company in India or hire Hadoop developers 

In 2019, the Hadoop market generated a revenue of $26.74 billion, and it is predicted to attain $340.35 billion by 2025 with a surging CAGR of 37.5% during 2020-2025. 

But what is Hadoop? 

Well, Hadoop is a powerful and interesting framework that makes big data look small via faster data processing by filing various different roles in an enterprise-related kind of data. 

Apache Hadoop is a collection of open-source software programs that facilitates deploying a network of various computers to solve problems, including a huge amount of computation and data. 

Undoubtedly, Hadoop is a wonderful framework for processing large amounts of data sets; however, it is definitely not the silver bullet that completely fits all the business operations for deploying big data analysis. 

So, if you are thinking of deploying Hadoop in your organization, ask these questions first! 

• Does my organization have various petabytes of data? 
• Is my organization data enough for Hadoop?
• Does my organization have genuine big data problems? 
• How much big data will be operated by businesses?

There are a plethora of good reasons that support the use of Hadoop in your business for managing big data analytics, distributing computational capabilities at a budget-friendly cost, processing large amounts of data created by IoT, social media, and other mobile technologies. 

Various top-notch organizations, such as Facebook, Walmart, Amazon, Yahoo, and eBay, are using Hadoop to manage their big data and boosting the adoption of Hadoop, Among other big data analytics companies. 

Honestly, Big data and Hadoop are the two most popular software programs that end up complementing each other in each way. So, if you think big data is a door lock, then Hadoop Is a key. Furthermore, big data is a complex and dubious concept, and Hadoop is an open-source program that helps manage big data.         

Furthermore, the best way to understand the relation between big data and Hadoop is by talking about the big data challenges and how Hadoop is the solution.                                                                                                                                                                                                                                                                                           Didn’t get it? Let’s understand this in detail! 

How Can Hadoop Solve Your Big Data Problems at Once? 

Currently, Hadoop is a framework that consists of various components and tools provided by a huge range of vendors. Further, the huge variety of tools are based on the expansion of the basic frameworks. 

So, today we are going to cover the complete topic in five most important parts that are as follows- 

• Why Hadoop is used in big data 
• How Hadoop manages big data perfectly 
• How does Hadoop process large volumes of data 
• Applications of Hadoop in big data 

Let’s understand these points one by one! 

1. Why should you deploy Hadoop in big data processing? 

Sometimes before, when Hadoop was not introduced, the collection and analysis of unstructured as well as structured data were like unachievable and time-taken tasks. 

But with the introduction of Hadoop and its core supporting components, these mundane and time taken tasks have become a lot easy and fast.

Enterprises that are running on the Hadoop framework have learned that the distributed nature of Hadoop, infrastructure management, tuning, and configuration is quite complex without the use of Hadoop. 

Following are some of the benefits that the Hadoop framework offers to the big organizations- 

• Easy to use, deploy, and launch Hadoop environment 
• Reduces operational challenges like infrastructure management 
• Emphasizes critical business growth
• Decreases the cost of innovation 
• No need of hiring a system administrator 
• Quickly scale your business 
• Zero tolerance for faults

Hadoop offers a foundation on which you create applications to manage, analyze, and process big data. Following is the list of Hadoop Big data tools- 

Apache Zookeeper 

It automates failovers and lowers down the impact of a failed NameNode. 

Apache Hive 

It is a data warehouse for analyzing and processing huge sets of data stored in Hadoop’s file system. 

Apache HBase 

It is an open-source non-relational database for big data processing. 

Apache Flume 

It is a distributed service for streaming huge amounts of logged data. 

Apache Pig 

It is a platform for creating jobs that run on Hadoop. 

Hadoop offers full insights due to the longevity of data storage. This longevity of data storage-related Hadoop also reflects the cost-effectiveness. 

2. How Hadoop manages big data perfectly?

Components of Hadoop helps in managing a large amount of business data. The core part of Hadoop, known as the MapReduce component, drives the deep analysis of collected data. This component is compatible with various tools for data analysis with certain settings. 

For instance- MapReduce is widely adopted and popular due to its supportive programming languages, including Java, Python, and Ruby. 

Further, as more and more businesses have started deploying Hadoop and contributing to the Hadoop development, the demand for Hadoop consulting has increased to an immense level. This tool also manages the raw data cost-effectively and efficiently. 

In addition, the open-source nature of Hadoop helps businesses to run their app on different servers. It also helps in performing robust analytical data management and analysis tasks. The open-source nature of Hadoop is one of the best features as it allows the app to run on different servers. 

Following is the list of Hadoop components- 

• MapReduce 

It directs the order of batch applications. 

• Hadoop Ozone 

It is designed to scale billions of objects of different sizes. 

• Hadoop submarine 

It offers data scientists/ infra engineers to run machine learning frameworks. 

• YARN 

It helps to run various types of distributed apps other than MapReduce. 

• Hadoop Common 

It refers to the collection of common libraries and utilities that support other modules. 

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3. How does Hadoop process huge volumes of data?

Hadoop is built to analyze and collect data from a wide range of sources. This framework helps the app to run on multiple nodes, which augment the volume of the data processed and received. 

Furthermore, Hadoop-based tools are able to store and process a large volume of data due to the node's availability, which is also seen as storage units to scale horizontally, designing more rooms and resources. 

Thus, the Hadoop components and tools allow the management and storage of big data because of the ability to carry out specific purposes and the main operational nature of Hadoop around all clusters. 

4. What are the applications of Hadoop in big data?

Businesses, mainly those who generate a plethora of data, depend on Hadoop and alike platforms for the analysis and storage of data. Businesses, such as Facebook, generates a massive amount of data and have been seen deploying Hadoop in their operations. 

The flexibility of Hadoop helps the business to function in various areas of Facebook in various capacities. Facebook data are compartmentalized into multiple components of Hadoop and its applications tools.

Besides Facebook, Amazon also uses Hadoop components to process inefficient data. The Hadoop component which is used by Amazon is the Elastic MapReduce web service. Elastic MapReduce web services are adopted for effectively running data processing operations, such as web indeed, log analysis, and financial analysis. 

Conclusion 

Thus, we have tried to explain in detail what is big data, Hadoop, and how Hadoop can help you get rid of all your big data problems. To deploy Hadoop in your business operations, you need to consult a big data development company or hire Hadoop consultants.

There are a lot of companies that offer Hadoop services, but ValueCoders is a top-notch big data application development company that has over 16 years of experience in delivering best-in-class services, including Hadoop consulting services and big data development services to start-ups, enterprises, and entrepreneurs. 

Don’t wait! Hire Hadoop developers or Consult ValueCoders now for more details!