Big Data and IoT
big data and IoT are two distinct concepts co-dependent on each other for better representation and attaining utmost success. The aim of both big data and IoT is the conversion of data into data-driven actionable insights.
We all know that companies utilize analyzed data to further grow their businesses by making prompt decisions and future maintenance. Many companies also store data for a preview of the performance of the company’s overtime. If a company combines real-time IoT and long-term insights of big data analytics, it can save more, manage the available resources effectively, and enhance the most efficiency. Looking forward to becoming a Data Scientist? Check out the Data Science Certification and get certified today
How to use big data?
Big data mainly provides support to IoT functioning, but in turn, has its challenges. Connected devices generate data, and utilizing IoT along with analytics will help make more profit-oriented decisions.
Processing of data includes a few steps. They are:
- There are four V’s of big data on which data is dependent. They are volume, velocity, variety, and veracity. The devices that are connected to IoT produce a huge volume of heterogeneous data, which are stored in big data systems.
- A big data system is a relatively widely-distributed system. This notifies that a huge amount of data is stored in the storage system.
- Analyzing tools are available that accurately analyze the collected data.
- Finally, after analyzing the collected data, a conclusion is reached that enhances the firm’s proper decision-making process. Also, check out the Best Data Science Training in Hyderabad to start a career in Data Science.
Challenges of big data analytics
The most significant challenges that are associated with IoT and big data are:
- Data storage and management
The collective data that is being continuously generated across various platforms is increasing day-by-day at a very high rate. But the storage capacity that confines these data does not pose infinite space. For this reason, frameworks are set up that handle these large amounts of data.
- Data visualization
The data that the connected devices generate are either structured or unstructured or, in some cases, semi-structured. They are collectively known as heterogeneous data. It is very complicated to visualize these data immediately. Therefore, data needs to undergo preparations for better understanding so that accurate business decisions can be made accordingly, which will improve the company’s efficiency. Become a Data Scientist with 360DigiTMG Data Science Certification Course in Chennai. Get trained by the alumni from IIT, IIM, and ISB.
- Maintaining confidentiality
All devices that are IoT-enabled generate enormous data that needs full privacy and encryption. The collected data should always remain confidential since it contains its users’ private information and, therefore, should be handled carefully.
- Integrity
Smart devices are very efficient in determining, sharing information, communicating, and even analyzing many applications. The data on these devices are leakproof; that is, they assure zero data leakage and piracy to their users. Some scale or condition of integrity must be introduced in the data assembly methods. Want to learn more about data science? Enroll in the Data Science Training institute in Pune to do so.
- Power captivity
The devices enabling internet connections require an infinite power supply for providing stable and continuous IoT operations. The devices that are connected lack sufficient memory, energy, and processing power. So, the mechanisms are accordingly light-weighted.
- Device security
Big data is vulnerable to external attacks. For this reason, analytics faces challenges in its venture of providing device security. These challenges are mainly because of the temporary storage and networking of IoT devices.
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