CLOUD HOSTING USING HADOOP

 

 

To understand cloud hosting using Hadoop, we first need to know the meaning of the following terms:

 

Cloud computing is the most current computational paradigm to emerge. It's an internet-based technology that allows any sort of business or organisation to employ advanced computer applications. Cloud computing promises to transform how individuals use computers to access, alter, and save personal and corporate data.

It refers to the distribution of computer services through the internet, such as storage, databases, servers, networks, software, and analytics. Companies that provide these services are known as cloud providers. These services are normally charged on a per-user basis. Users may utilise the cloud to host websites/blogs, create new applications/services, stream music and video, and analyse trends and make predictions, among other things.

 

Big Data: refers to the massive amounts of data created by businesses on a regular basis. Velocity, Volume, Variety, Veracity, and Value are the five V's that best describe it.

The amount of data created/generated on a daily basis is referred to as Volume. Every 40 months, the volume of data is expected to double.

The speed at which data is acquired is referred to as Velocity. Velocity is critical because any business can remain competitive if it can analyse enormous amounts of data in real-time.

The numerous data sources used to collect data, such as social media, cellphones, and photographs is the Variety.

The worth of the data being gathered is referred to as Value. Large amounts of data are useless unless they can be used to provide value to the company or individual collecting it.

 

The accuracy and trustworthiness of the data is referred to as the Veracity.

Hadoop: is a distributed computing paradigm that is particularly good at processing large amounts of data.

Hadoop is made up of three key components, which are as follows:

HDFS (Hadoop Distributed File System) is a distributed file system that facilitates data processing on low-cost computers.

Hadoop MapReduce — This capability enables the distribution and simultaneous processing of massive amounts of data across computer clusters.

Hadoop Yarn — This functionality allows for more effective cluster resource management.

Cloud computing is a trend that is affecting technological advancement and, as a result, has resulted in vast amounts of electronic data. The phenomenon known as Big Data arose as a result of this massive amount of electronic data. Big Data and Cloud go hand in hand since the former is concerned with the storage capacity of the cloud system, while cloud computing makes use of vast storage and processing capabilities. Big data sets the path for the rapid development of cloud computing by providing computational capabilities to big data applications.

The two technologies, cloud computing and big data are mutually beneficial. While the fast increase of big data is seen as a concern, cloud computing is expanding to bring answers. While traditional storage systems are incapable of handling large amounts of data, cloud computing is growing in popularity due to its data splitting strategy, which allows it to absorb massive amounts of data. The process of storing data in more than one place or availability region is known as data splitting.

When it comes to the Cloud, it is hailed as a viable answer to the looming challenges of processing big and complicated data volumes. This is due to its quickness and flexibility in processing large amounts of data, which necessitates a lot of computational power. The cloud is also the finest platform for both structured and unstructured data processing. In other words, combining Cloud with Hadoop is no longer a choice, but a requirement.

To enhance flexibility, availability, and cost control, companies frequently prefer to host Hadoop clusters on public, private, or hybrid cloud resources rather than on-premises infrastructure. Many cloud solution providers, such as Google Cloud's Dataproc, provide fully managed Hadoop services. Operations that used to take hours or days may now be accomplished in seconds or minutes, with companies just paying for the resources they use with this type of bundled Hadoop service.

Google, IBM, Amazon, and Microsoft are just a few of the firms that have effectively adopted Big Data in the Cloud. The cloud environment should be changed to suit both data and cloud for a good match between the two technologies. On the cloud, changes such as CPUs to handle huge data should be made.

The Benefits of Using Hadoop in the Cloud:

·       Insufficient space - You may want Hadoop clusters, but you lack the space to house racks of physical servers, as well as the requisite power and cooling.

·       Flexibility- It's considerably easier to reorganise instances or extend or shrink your footprint for changing business demands without having to rack up real servers or run cables. Everything is managed using the APIs and online interfaces of cloud providers. Changes can be programmed and implemented manually or automatically and dynamically, depending on the situation.

      New use patterns have emerged.- The ability to make modifications in the cloud allows for new use patterns that would otherwise be impossible to implement. Individuals, for example, can have their own instances, clusters, and even networks without a lot of administrative overhead. The total CPU core budget in your cloud provider account can be concentrated in a small number of large instances, a greater number of smaller instances, or a combination of the two, and it can even alter over time. 

          The rate of change is increasing.- Purchasing, unpacking, racking, and configuring physical computers takes far longer than launching new cloud instances or allocating new database servers. Similarly, unwanted cloud services may be quickly decommissioned, whereas unneeded hardware tends to sit idle.

·       Reduced danger- How much on-premises gear do you need? The entire business slows down if you don't have enough. You've squandered money and have idle gear that continues to waste money if you acquire too much. Because you may adjust how many resources you utilise on the cloud fast and easily, there is no risk of undercommitment or overcommitment. Furthermore, if a resource fails, you don't have to fix it; you may just reject it and assign a new one.

      Focus- Instead of investing time and effort on the logistics of procuring and maintaining its own physical hardware and networks, an organisation that rents resources from a cloud provider may focus on its key capabilities, such as employing Hadoop clusters to run its company. For a tech company, for example, this is a significant benefit.

·       Availability in every country- The biggest cloud providers have data centres all around the world that are ready to go right now. You can get the greatest results by using resources near to where you work or where your clients are. You may build up redundant clusters, or even full computing environments, across various data centres so that if one data centre experiences local issues, you can migrate to another.

      Needs for data storage.- If you have data that is required by law to be stored in specified geographic locations, you can store it in clusters hosted in those data centres.

           Features of a cloud provider- Each major cloud provider has a set of capabilities to support the compute, networking, and storage operations. Your clusters should also operate in the cloud provider to get the most out of those functionalities.

·       Capacity- Few users put a strain on a big cloud provider's infrastructure. Large systems may be set up on the cloud that isn't nearly as straightforward to set up, let alone manage, on-premises.

      Conclusion: 

      This blog explains the important terms to cloud hosting, Big Data, Hadoop, why hadoop is used in cloud hosting and cloud computing and how it is beneficial.