- Main Menu
- Platform Overview
- Automated Root Cause Analysis
- Hybrid Cloud Monitoring
- Multi-Cloud Monitoring
- Network Monitoring
- Network Configuration Management and Change Management
- Trust Center
- Technology Partners
- Why ScienceLogic
- AIOps Value CalculatorCalculate Your Value
Build your AIOps use case in 3 easy steps.
- Main Menu
- By Industry
- By Solution
- By Use Case
Big Data Analytics
Big Data Definition
Big data are large, diverse sets of structured, semi-structured, and unstructured data, generated and stored by modern enterprises, and often described as being one petabyte or larger in aggregate size and characterized by volume, variety, and velocity—or the three Vs of data. Big data is regarded as a source of valuable business intelligence, but because of the size, diversity, and complexity of the data sets, they are beyond the capability of humans or traditional digital systems to manage and thus require powerful, specialized systems to conduct complex analytics to extract useful insights.
Common sources responsible for the aggregation of big data include electronic devices, internet-connected devices, social media interactions, web traffic and transactions, business and industrial applications, online transactions, log files, and network traffic. By some estimates, 2.5 quintillion bytes of data are created daily.
What are big data analytics?
Big data analytics is the use of sophisticated algorithmic processes to evaluate large, diverse sets of data by identifying previously unknown patterns, trends, and correlations between the various sources. Business intelligence derived from big data analytics supports better decision making, IT workflow automation, and predictive modeling.
What is the difference between big data analytics and data science?
Data science is the craft of conducting meaningful analysis of data, whereas analytics are the processes by which the craft is applied.
What do big data analytics have to do with the cloud?
Cloud computing—the use of remote data center resources to provide on-demand storage, services, and applications—makes it possible for more human and machine interactions to take place, exponentially increasing the potential for data creation. The vast computational resources available via the cloud also make powerful systems—once reserved for a small number of large government, business, and research institutions—widely available for smaller organizations to conduct big data analytics.