For the past decade, global data growth has been exponential and shows no signs of slowing down. The proliferation of data-gathering technologies has led to a situation where data is being created at an unprecedented rate. Big data refers to the massive amounts of structured, semi-structured, and unstructured data that come in large volumes from various sources at a high velocity (OCI, 2022). Given big data’s sheer size, volume, and speed, conventional data processing tools cannot handle these datasets (Sagiroglu & Sinanc, 2013).
What is Big Data Analytics?
Big data analytics studies large data sets to discover previously unknown connections or patterns. By analyzing this massive amount of data, businesses gain valuable insights that strengthen their ability to make sound business decisions and gain competitive advantage. Big data is now a prominent topic among business owners, executives, and investors from the fashion and transportation industries to small and medium-sized businesses (Future of Everything, 2020). Researchers and corporations alike have begun to pay more attention to the topic as the necessity for analyzing massive datasets to discover trends and patterns continues to develop.
What are the Differences Between Big Data and Conventional Data
The main difference between big data and conventional data can be seen in the three distinguishing traits of big data; volume, velocity, and variety (Sagiroglu & Sinanc, 2013). Conventional data used daily is stored in traditional databases in a structured format. All businesses, from the smallest startups to the largest multinationals, maintain large amounts of conventional data, also known as structured data. Traditional data is stored and managed in conventional databases, mainly centralized database architectures. The volume of traditional data ranges from Gigabytes to Terabytes, and it is data that is primarily generated within the enterprise on a daily, hourly, or weekly basis. Data-related tasks require using standard database administration tools and the structured query language (SQL) to manipulate and manage data.
On the other hand, big data can be seen as an improved form of conventional information. When dealing with big data, the data sets are too large or too complex for the typical data-processing software to handle (Kitchin & McArdle, 2016). Big data is characterized by its volume, velocity, and variety, or the 3Vs. These features distinguish big data from traditional data in significant ways. The sheer volume of big data directly results from the wide range of formats and data sources, with volumes ranging from Petabytes to Zettabytes or Exabytes. The three formats of big data include: structured, semi-structured, or unstructured (Technology, 2022), unlike conventional data, which is structured data. The proliferation of IoT devices means that companies now receive and must process data at a rate that was previously unimaginable. Big data processes data in real-time to meet the changing business needs that happen by the second (Kitchin & McArdle, 2016).
Public Sites with Free Datasets for Big Data Analytics
Several public sites provide free access to varied datasets. Nowadays, information, like anything else, can be found on Google. Google Dataset Search, which debuted in 2018, is essentially Google’s regular search engine, but for data only (Hillier, 2022). Google Dataset Search aggregates external data and provides a clear summary, description, source, and update of the data source. While the datasets themselves are freely accessible, the search-based results may incur a cost. As the site offers a wide variety of data compiled by Google on various topics, it is essential to use appropriate keywords when searching for datasets in the Google Dataset Search.
The main objective of data analysts is to aid in making informed business decisions. Datahub provides datasets related to business and finance on Datahub.io (Hillier, 2022). Access to this site is mostly free, with no registration needed. The primary areas of interest are stock market data, property prices, inflation, and logistics, but it also covers a wide range of other topics, such as climate change and entertainment. The portal contains a wealth of information that is regularly updated on a monthly (or even daily) basis, facilitating access to the most recent findings and information from various time periods.
Future of Everything. (2020). Expert predictions: The future of big data and business 20 years from now. Future of Everything. https://www.futureofeverything.io/expert-predictions-the-future-of-big-data-and-business-20-years-from-now/
Hillier, W. (2022). 10 great places to find free datasets for your next project. Careerfoundry. https://careerfoundry.com/en/blog/data-analytics/where-to-find-free-datasets/
Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 2053951716631130. https://doi.org/10.1177/2053951716631130
OCI. (2022). What is Big Data? Oracle. https://www.oracle.com/big-data/what-is-big-data/
Sagiroglu, S., & Sinanc, D. (2013, 20-24 May 2013). Big data: A review. 2013 International Conference on Collaboration Technologies and Systems (CTS),
Technology, T. (2022). Big Data vs. Traditional Data: What’s the Difference? Treehouse Technology Group. https://treehousetechgroup.com/big-data-vs-traditional-data-whats-the-difference/#:~:text=While%20traditional%20data%20is%20based,better%20performance%20and%20cost%20benefits.