Big data is the buzzword in the IT industry. Organizations of all sizes are recognizing the value of data and are using it to quantify performance. Big data can also help recognize challenges and detect new opportunities for growth. There is no denying the numerous benefits of using big data. However, you need a massive amount of computing resources and advanced software systems to incorporate big data within your organizational system. The cloud has helped organizations take big strides in filling the need for big data. It comes with inexhaustible computing resources, making big data in the cloud initiatives a distinct possibility for businesses of all types and sizes.
Big Data in the Cloud
While big data and computing are two entirely different concepts. In the world of information technology, they have become inseparably interwoven.
Big data refers to the colossal amounts of data generated in various forms in an enterprise. They can be structured, semi-structured, or unstructured and are derived from diverse sources. Big data is also about processing massive volumes of data to find solutions to a problem or identify a trend.
Analysis of big data is done through a series of mathematical algorithms. The analysis process varies with the type and source of data and the intent of the analysis.
The sheer volume of computing and the size of networking infrastructure required companies to build a big data facility. There is also a substantial financial investment in servers, storage, and dedicated networks. A high level of software expertise is required to set up an effective computing environment.
Cloud computing provides organizations with on-demand computing resources and services. Indeed, the desired infrastructure and storage resources can be easily set up. Experts can perform detailed analyses in the cloud. The cloud allows access to unlimited resources across the public cloud. These resources can be used for as long as needed, and the environment can be dismissed later. Companies will have to pay only for the resources and services actually used.
A cloud offers resources and services on demand. It means businesses don’t have to build, own or maintain the infrastructure. The cloud thus enables easy and affordable access to big data technologies for companies of all sizes.
The Pros of Big Data in the Cloud
The cloud brings a variety of significant benefits to businesses of all sizes. Some of the most immediate and substantial benefits of big data in the cloud include the following.
A standard business data center has many limitations while managing physical space, power, and cooling. They also need the funds to purchase and deploy the hardware required to build a big data infrastructure. A public cloud, in comparison, can manage tens of thousands of servers spread across multiple global data centers. Companies can have access to readily available infrastructure and software services. They can quickly put together any infrastructure for data projects of almost any size.
Every data project is different. While one project may need 100 servers, another may need double that number. With support from the cloud, companies can deploy the resources needed to achieve their goals. They can be released when the task is accomplished.
Setting up a business data center involves enormous capital expenses. Apart from hardware, businesses also need to spend heavily on facilities, power, and ongoing maintenance. In the cloud, all those expenses combine into a flexible pay-per-use model.
Data forms the core of big data projects in terms of value. So, the benefit of cloud resilience lies in data storage reliability. Clouds are designed to replicate data and can ensure high availability in all resources.
The Cons of Big Data in The Cloud
Public clouds and various third-party big data services have a brilliant track record in big data use cases. However, the cloud is not all about benefits. Businesses must also take into account the potential drawbacks.
Cloud users depend hugely on network connectivity from the LAN. If there are outages along the network path, it can cause increased latency or, worst, complete inaccessibility. An outage may not affect a big data project, but outages can spell bad news for users.
Cloud data storage can result in a considerable long-term cost for big data projects. The three key aspects are data storage, migration, and retention. Certainly, loading large volumes of load into the cloud may take a long time. Plus, there is a monthly fee for storage instances. Any additional movement may cost companies extra. Retaining redundant data costs money. Businesses must have effective data retention and deletion policies to manage cloud storage costs better.
Big data in big data projects may have proprietary issues that come under data protection and other regulations. Users must take adequate steps to maintain optimal data security in cloud storage. This involves authentication, authorization, and encryption while in storage or transit.
Lack of standardization
There is no standard approach to implementing or operating a big data deployment in the cloud. This can result in poor performance and put the business at risk. All big data architecture needs suitable documentation. Care also needs attention to adhere to the policies and procedures.
If you want your business to remain competitive, you have to choose the big data in a cloud solution. Choosing the right cloud deployment model is important. There are four models to choose from – public, private, hybrid, and multi-cloud. It’s also important to understand the nature and tradeoffs of each model.
The private cloud provides better control but can be very expensive because a business must own and operate the entire infrastructure. The public cloud combines the benefits of on-demand resources and scalability. However, users are in charge of managing cloud resources and services. A hybrid cloud is recommended when sharing resources. However, they are complex to build and manage. Multiple clouds offer the benefits of availability and use cost. However, no two clouds have the same resources and services. This makes multiple clouds more complicated to manage.