For every task you accomplish online on your smartphone today, you are generating incredible amounts of information. You are leaving a digital footprint whenever you are posting something on Facebook, searching a keyword on Google, booking an airline ticket, transferring money through UPI, watching a video online, and more. All of such high volumes of data, commonly known as big data, are being gathered by companies to make smarter business decisions. The use of technologies like cloud computing and increased computing power of devices has allowed big data to become more usable and useful.
Apart from the high volume, the term big data also refers to different kinds of resources from where it is generated, the rate at which it is changing, and its complex format. Such large data sets cannot be processed by the traditional database management systems and require innovative forms of information processing. When applied to any business domain analyzed effectively, it can reveal patterns and invaluable correlations that often go unnoticed and can enable business leaders to derive innovative solutions and serve customers better.
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Big data engineering is an ocean of opportunities for professionals who have good analytical skills and love to dive deep into unstructured data and uncover meaningful patterns. Big data engineers are skilled in using Hadoop-based technologies like MapReduce or MongoDB, Apache Spark, programming languages like Python or R, and various other tools to design big data solutions. Companies also expect them to be proficient in data warehousing, data extraction, data transformation, ETL processes, and data pipeline construction.
Big data analytics has penetrated every industry today, be it aviation, healthcare, retail, manufacturing, telecom, information technology, or finance. Companies have so far achieved significant benefits like gaining data-driven insights for better decision making, increasing business user productivity, and decreasing their expenses through optimized use of resources. These benefits further result in improved customer service, achieving higher Return on Investment (ROI), frequent changes to business strategies, get ahead of competitors and ensure faster time-to-market of their products.
Though these benefits can lure companies to adopt big data right away, it is important to note that the adoption of any new technology or approach comes with its own set of challenges, and so is big data technology. There are some challenges that companies may face when trying to implement big data analytics initiatives. This article explores some of the major challenges that appear on the path of big data adoption.
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Challenges Related to Big Data
Let us look at some of the obvious challenges that are related to big data analytics.
A Vague Understanding Of Big Data
Big data has created a lot of hype, lately, and companies are hurrying into adopting it or some change the existing processes for the sake of its adoption. As a result, there are hardly any employees who know the basics of big data, where it can be adopted, or what infrastructure would be needed. If the project heads are unclear how to optimally utilize big data, the chances are high that they will waste a lot of time and resources and increase the risk of project failures.
Improper Planning Of System’s Maintenance And Support
If the considerations related to the maintenance and support of systems are not taken into account in the planning stage itself, it can lead to many problems. One of the common problems that may arise is the issue of upscaling. Big data can grow dramatically, may be quite contrary to your expectations. If your big data solution’s design isn’t appropriate, there will be a lot of complexity involved in upscaling the storing capacities. The process may affect the system’s performance, and you may not be able to accommodate it within your budget.
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Data Ingestion Issues
When you start working with big data, the first step is to collect data from disparate sources and prepare them for analysis. During this process, you may come across challenges related to data integration. The data can come from multiple sources like social media streams, organization applications, electronic sensors, email systems, website logs, or employee-created documents. It can be quite difficult to collect that unstructured data and clean it so as to use them for creating reports. Data integration and ETL tools can be used to solve such problems but some of them may still persist.
Finding The Right Talent
A company needs to find technically sound professionals to work on big data projects. Not many professionals have an in-depth understanding of big data and those who have years of experience working in this domain. Skills like knowledge of Apache Spark, Hadoop, MapReduce, MongoDB, data management frameworks, and in-memory analytics are sought from big data engineers. To deal with a talent shortage, companies are ready to pay even higher salaries and are considering providing on-premises training programs on big data.
Managing Big Data Related Costs
A lot of expenses are incurred to manage big data right when an organization adopts it up to the launch of products. When setting up the required frameworks and maintaining them, using cloud services, developing and configuring new software, a hefty sum is spent by the organizations. Working on big data also involves the hiring of skilled developers, big data engineers, and network administrators; this step further leads to an increase in costs.
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Though there are some big data challenges companies face while adopting it, proper research and planning can help in overcoming them. Big data analytics is a promising technology, and you should consider starting a career in this domain. An online big data course can help you gain the required skills.
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