Big Data refers to data sets with exceptionally large, very complex or very rapid information which can’t be analyzed sufficiently through conventional relational databases. Big Data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision making, and process automation.
Big Data is so large and complex that none of the traditional data management tools can store it or process it efficiently. Big data is when the normal application of current technology does not enable users to obtain timely, cost-effective, and quality answers to data-driven questions.
The bulk of big data generated comes from three primary sources:
- Social data: social media platforms (tweets, comments, video uploads, audios)
- Machine data: sensors like road cameras, smart meters, satellites, IoT, robots, and web logs
- Transactional data: Invoices, payment orders, storage records, delivery receipts
Big data management is the organization, administration and governance of large volumes of both structured and unstructured data. The goal of big data management is to ensure a high level of data quality and accessibility for business intelligence and big data analytics applications.
Reliable data storage and data management are done by Data Centres. Today’s data centers have features such as 24×7 availability, security, scalability and performance. Ensuring that all these requirements are met is a power-intensity endeavour that results in rising CO2 emissions. Data Centres currently consume a large amount of energy (about200 terawatt hours per year), and the resulting emissions are expected to e greater than those produced by the entire aircraft industry.
A data fabric brings together multiple data sources to provide you with a centralized view of your entire data landscape. By providing end-to-end data management capabilities, a data fabric ensures that various kinds of data can be combined, accessed, and governed, benefiting business users, data scientists, and engineers alike. By using a data fabric architecture, businesses can continue to use disparate data sources and storage repositories (databases, data lakes, data warehouses) they’ve already invested in while simplifying data management.