Big data is changing our world, more often changing stakeholders' approaches in dealing with complex and huge volumes of data. A single movement on a digital platform generates the bulk of data piled up in massive data sets. This stored data can lead to better decision-making if analyzed properly. However, multiple challenges make it hard to deploy big data infrastructure.
Big Data Infrastructure
Processes in the proper place and analytic tools are not enough to get a better experience of big data-based systems; hence you also have to optimize your cloud big data infrastructure. The big data infrastructure requires various tools and agents to collect data. It also requires physical storage devices and software systems for storing purposes. The application environment is an essential requirement for big data infrastructure. It hosts the analytic tools for analyzing the data and then backing it through archive infrastructure.
Big Data Challenges
Although, the deployment of any new technology is never so easy. It brings various limitations and complexities. In the same way, the deployment of big cloud data is not an easy job to perform. In the presence of a huge amount of data, many things can get disturbed by the various components mentioned above. This article will highlight some of the major challenges that make it hard for you to convert big data into value.
Lacking Skilled Professionals
These modern technologies like cloud big data require skilled professionals to properly understand tools and giant data sets. Some of the important roles that are familiar with the bulk of data and are skilled with data handling tools are data scientists, data analysts, and data engineers. Lacking skilled professionals is a high-level challenge faced by most companies, primarily because of the rapid growth of data handling tools.
Insufficient Understanding of Big Data
Many companies have failed in their big data initiatives just because of an inadequate understanding of big data and its infrastructure. Data professionals might only know what data do but do not have a clear picture of its storage, analysis process, and how it is managed. This challenge is crucial, as an insufficient understanding of big data while implementing can be a reason for your organization’s downfall.
Expensive Deployment of Cloud Big Data
The adoption of big data requires many expenses. The option on-premises solution is quite expensive, as you will have to take the burden of new hardware, new lines, electricity expenses, and many other things as well. Although the frameworks are open-source, you have to manage the cost of different new software components like development, setup, and maintenance.
If you opt for a cloud-based big data solution, you still have to mind the costs of cloud services, big data solution development, and maintenance. Both results clearly state that deploying big data infrastructure is very much expensive.
Ignoring Big Data Security
Security should be the number one priority for all organizations working in any kind of field. The other systems never neglect this sort of challenge. However, the adoption of cloud big data projects neglects this factor and shifts it to other stages. A company must not spend all of its efforts and time on understanding, storing, managing, and analyzing huge data sets. Instead, they must prioritize security at an early stage of software development. This threat can lead to a data breach or data theft that can cause severe damage to the organization.
Managing Data Quality
This is another challenge that has to be faced throughout big data-based systems. The problem of data integration arises at the time when we receive data of different formats from various sources for analyzing process. The best example that will elaborate on this challenge is that e-commerce companies need to analyze data from different social media platforms, website logs, etc. Data formats are likely to have a different format from other sources, whereas matching them can be a serious problem for us.
Another challenge related to it is how much your data is reliable. Unreliable data will not only contain repeated data but will also lead to contradictions. Therefore, managing and making efforts to collect authentic and dependable data is a serious challenge for many organizations.
Network Connectivity Issue
Transferring the bulk of data across the network will take more time, especially if the network demands using public internet. This networking requirement limits the bandwidth as compared to internal organization networks. Extra bandwidths may solve this issue, but it will cost a lot. However, this challenge can be resolved by developing the cloud big data infrastructure to reduce the volume of data transfer over the network.
Rapid Data Growth
This is the most common challenge of big data infrastructure that needs immediate attention. It becomes difficult to handle and store data properly in data centers and databases of companies as data continues to grow exponentially with time. The unstructured data makes it impossible to find them in the database. However, many companies have made efforts to eliminate this challenge by implementing techniques like compression and duplication.
Another unique technique, data tiering, permits the company to store data in many storage tiers. It makes sure that data is stored in the appropriate space. Companies can also opt for cloud big data tools like NoSQL and Hadoop.
Final Verdict
All the above-mentioned challenges can quickly be resolved with a proper and efficient big data solution. A well-organized and adequately designed solution can eliminate these challenges effortlessly. However, companies should also take a more systematic approach towards it and should provide proper guidance to employees about cloud big data adoption. They should hold seminars and workshops to share essential facts about the big data infrastructure. Read more about it on our site and check out more interesting and informative tech articles.
They need to prioritize big data security at all costs to prevent data theft. The company should work on minding the costs and develop plans for future upscaling.
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