"Big data" is a buzzword these days due to an enormous amount of data-rich applications in different industries and research projects. In practice, big data often take the form of data streams in the sense that new batches of data keep coming over time. One fundamental research problem for analyzing such big data streams in a given application is to sequentially monitor the underlying process behind the observed data to see whether it is longitudinally stable, or how its distribution changes over time. To monitor a sequential process, one major statistical tool is the statistical process control (SPC), which has been used mainly for monitoring production lines in manufacturing industries during the past several decades. With many new and versatile methods developed in recent SPC research, SPC can provide a powerful tool for handling some big data applications. In this talk, I will introduce some recent SPC concepts and methods, and discuss some challenges in the interface of the existing SPC methods and some big data applications.