Since the beginning of this decade, big data has evolved as a promising term to realize data intensive projects and applications, which seemed impossible years ago. Using new technologies and techniques massive amounts of differently structured data, taken from various sources, can be easily stored, processed and visualized. Especially enterprises are aware about its potentials at several stages, such as the improvement of knowledge generation, organizational agility, business process, and competitive performance.

However, for an enterprise, a big data project is a huge investment that could lead to a crucial change on various levels, especially in terms of the infrastructural part. Thus, introducing a big data project is a strategic decision that needs to be made based on solid understanding.

In course of this development, multitudes of operations are nowadays used for the preparation as well as the actual analysis of the data. Apart from very specific operations, that deal with the analysis or visualization of the data, also preparatory steps, dealing for instance with the collection, cleansing and transformation, are included.

The main goal of this work aims to discover, describe and classify all existing operations in the context of big data. The actual gathering of information can be made realized by a structured literature research and/or the use of various tools and techniques.

The scope may vary depending on the selected form.