Big data is exactly what it sounds like: the use of extremely large and/or extremely complex datasets that stretch the capabilities of standard BI and analytics tools. 

As this definition suggests, there are several qualities that make big data distinct from traditional data analytics methods:

Volume: The data may be intimidating due to its sheer size.
Variety: The data may come in many different forms or file formats, making it harder to integrate.
Velocity: The data may arrive very rapidly in real-time, requiring you to constantly process it.
Variability: The data’s meaning may frequently change, or the data may have serious flaws and errors.
Dealing with big data is one of the greatest challenges for modern BI and analytics workflows. The good news is that when implemented correctly, ETL (and other data integration methods) can help you get data from multiple sources and generate better insights such as e-commerce metrics so you can make smarter data-driven decisions.