What exactly is Big Data?
Big data (or massive data) is a term that refers to large or complex data sets that cannot be processed by traditional data processing applications. It can also be defined as a large amount of unstructured or structured data from various sources.
Big data isn’t sampling but observing and tracking what’s happening. As a result, big data often contains data sizes that exceed the capabilities of traditional software to process in an acceptable amount of time.
Big data analytics is gaining prominence in modern research due to recent technological advancements. It helps ease of publishing new data and the high level of transparency required by most governments around the world.
They cover a lot of business every day. However, it is not the amount of data that matters. It is how an organization handles important data. Insights from big data can be analyzed for better decision making and strategic business changes.
Definition of Big Data
Companies use big data in their systems to improve operations, better customer service
Big data consists of gigantic datasets that often exceed the human capacity to collect, use, manage, and process them in an acceptable amount of time. The size of big data changes frequently, and as of 2012, the size of a single data set ranges from terabytes (TB) to tens of petabytes (PB).
Big data must be counted, compared and analyzed by computer to obtain objective results. Data mining is the study of methods for analyzing big data.
Features of Big Data
It has 4 basic characteristics.
1) Huge amount of data 2) Variety of data types 3) Fast processing 4) Low value density* *Big data stores a lot of useful data for the company (huge value) but the information that is valuable is obtained only after analyzing a lot of data (low density)
The role of Big Data
Cloud computing provides storage and computing platforms for these massive and diverse big data. Through the management, processing, analysis and optimization of data from different sources, and the results are fed back to the above applications, huge economic and social value will be created.
The processing and analysis of big data is becoming the node of the new generation of information technology fusion applications. Mobile Internet, Internet of Things, social network, digital home, e-commerce, etc. are the application forms of the new generation of information technology, and these applications continue to generate big data.
Big data has the power to generate social change. But unleashing this energy requires rigorous data governance, insightful data analysis, and an environment that inspires management innovation.
Big Data Technology
Examples MongoDB, Redis, and Cassandra
Big data requires special techniques to efficiently process large volumes of data that tolerate elapsed time. Technologies applicable to big data, including massively parallel processing (MPP) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.
When it comes to big data, Hadoop is the first technology that comes into play. This is based on map-reduce architecture and helps in the processing of batch-related jobs and process batch information.
Problems faced by Big Data
With the explosive growth of big data applications, it has derived its own unique architecture, and has also directly promoted the development of storage, network and computing technologies. After all, dealing with the special needs of big data is a new challenge. The development of hardware is ultimately driven by software requirements. The demand for big data analysis applications is affecting the development of data storage infrastructure.
On the other hand, this change is an opportunity for storage vendors and other IT infrastructure vendors. With the continuous growth of structured and unstructured data and the diversification of analytical data sources, the previous design of storage systems has been unable to meet the needs of big data applications.
Other challenging issues are delay problem, security, investment, accumulation of data, flexibility of storage systems, customized infrastructure for specific applications.
