Why Moving Your Big Data Project to the Cloud Is Always a Good Idea
As more and more employees are working remotely, access to information without physical presence became an absolute necessity.
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The rise of big data usage in cloud computing and cloud stores paved the way for the emergence of big data. After the start of the COVID-19 pandemic, those who had doubts about the convenience of having the data in the cloud finally understood they were wrong. As more and more employees are working remotely, access to information without physical presence became an absolute necessity. So, cloud computing is simply the commodification of computing time and data storage using standardized technologies.
It has significant advantages over traditional physical deployments. However, cloud platforms come in different forms and sometimes need to be integrated into traditional architectures.
This leads to a dilemma for decision-makers who are responsible for big data in cloud projects. How and which cloud computing is the optimal choice for your computing needs, especially if it is a big data project? These projects regularly show unpredictable, bursting, or immense computing power and memory requirements. At the same time, business stakeholders expect fast, inexpensive, and reliable products and project results.
Big Data Cloud Provider
A decade ago, an IT project or start-up that needed reliable and internet-connected computing resources needed to rent or locate physical hardware in one or more data centers. Today anyone can rent computing time and storage of any size. The range begins with virtual machines that are barely powerful enough to serve websites. Cloud services are mostly pay-as-you-go, meaning anyone can enjoy a few hours of supercomputing power for a few hundred dollars. At the same time, cloud services and resources are distributed globally. This configuration ensures high availability and durability that most, even the largest, organizations cannot achieve.
Consequently, the choice of a cloud platform standard has an impact on which tools are available and which alternative providers are available with the same big data processing technologies.
Cloud computing uses the visualization of computing resources to run numerous standardized virtual servers on the same physical machine. With this, cloud providers achieve economies of scale that enable low prices and billing based on small time intervals, e.g., B. every hour.
This standardization makes it an elastic and highly available option for computational needs. Availability is achieved not by spending resources to ensure the reliability of a single instance, but by making them interchangeable and having an unlimited pool of spares. This affects design decisions and requires that instance errors be properly resolved.
The effects on an IT project or the company using cloud computing are significant and change the traditional approach to planning and using resources. Resource planning becomes less important. For calculation scenarios, it is necessary to determine the feasibility of a project or product. However, provisioning and automatically removing resources based on demand must be geared towards being successful. Scaling both vertically and horizontally becomes practical once a resource is easy to implement.
Horizontal scaling refers to the ability to replace a single small computing resource with a larger one to accommodate increased demand. Cloud computing supports this by providing different types of resources for the switch. This also works in the opposite direction, namely to switch to a smaller and cheaper instance type when demand decreases. Since cloud resources are usually paid for on a usage basis, there are no hidden costs or investments blocking fast decision-making and adaptation. Despite the planning efforts, the demand is difficult to anticipate and naturally leads in most traditional projects to resources that are too much or too little available. As a result, traditional projects tend to waste money or get poor results.
Big Data Cloud Solution — Challenges
Vertical scaling increases elasticity by adding additional instances, each of which fulfills part of the requirement. Software such as Hadoop was specifically designed as distributed systems to take advantage of vertical scaling. You work on small, independent tasks on a massively parallel scale. Distributed systems can also serve as data stores like NoSQL databases, e.g., B. Cassandra or HBase or file systems like Hadoop HDFS. Alternatives like Storm offer coordinated stream data processes in near real-time across a cluster of machines with complex workflows.
If you also want to entertain yourself exploring Big Data Frameworks for cloud computing, you can read this article with a detailed comparison.
The interchangeability of resources together with the distributed software design absorbs errors and, equivalently, the scaling of virtual computer instances undisturbed. Spiking or burst requirements can be taken into account just as well as continuous growth.
Renting virtually unlimited resources for short periods of time enables one-time or periodic projects at low cost. Data mining and web crawling are great examples. It is conceivable to crawl huge websites with millions of pages in days or hours for a few hundred dollars or less. Inexpensive small virtual instances with minimal CPU resources are ideal for this purpose, as most of the crawling on the web waits for IO resources. Instantiating thousands of these machines to reach millions of requests per day is easy and often costs less than a fraction of a cent per instance hour.
Of course, such mining operations should take into account the resources of the websites or application interfaces that use them, respect their terms and conditions, and not hinder their service. A badly planned data mining process is equivalent to a denial-of-service attack. Finally, cloud computing is of course well suited to storing and processing the large amounts of data that are accumulated from such operations.
Published at DZone with permission of Eugenia Kuzmenko. See the original article here.
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