Big Data Analytics: How It Works and Its Benefits

The 5 main types of big data analytics are predictive, prescriptive, descriptive, diagnostic, and text analytics.

In the context of artificial intelligence, big data refers to the large and complex datasets that are used for training and enhancing AI models. Big data is vital to AI applications as it provides the raw material AI algorithms need to learn and make decisions or predictions.

In a nutshell, big data refers to the massive volume of data that is collected in various forms and from multiple sources, including customer surveys, emails, social media engagement, prior purchase histories, and so on. Big data analytics refers to the step-by-step process of analyzing these large volumes of data to gain actionable insights and make informed decisions for achieving specific goals.

No, big data and SQL are not the same. Big data refers to the management and analysis of extremely vast and complex datasets that traditional data management tools and systems cannot handle. Meanwhile, SQL is a domain-specific language that helps to manage and query structured data in relational databases.

Big data analytics is the type of analytics that prioritizes extracting insights, patterns, trends, and other key information from complex, vast datasets.