Data analytics and big data are terms that often collocate and can be confused to mean the same thing. Data analytics is about finding patterns within data, typically structured data, within significantly smaller sets than Big Data sets. Statistical analysis is a primary tool for data analytics. And the purpose is usually business problem-oriented.
Big Data analytics, however, is characterized by a high variety of structured, semi-structured, and unstructured data, drawn from various sources like social media, mobile, smart devices, text, voice, IoT sensors, and web, and further by the high velocity and high volume at which its data pipelines ingest.
Though there is no official big data size, big data operations can be measured in the terabytes and petabytes for organizations like eBay and Walmart, and in the zettabytes for Google or Amazon. Once collected, data can reside in an unstructured form in data lakes available for processing by data preparers. After processing, the filtered and structured data is maintained in data warehouses to be used by data consumers.