There is an urgent need for unifying data analytics as more and more application tasks become more complex: Nowadays, it is normal to see tasks performing data preparation, analytical processing, and machine learning operations in a single pipeline. Despite this need, achieving this is still a dreadful process where developers have to get familiar with many data processing platforms and write ad hoc scripts for integrating them. This tutorial is motivated by this need from both academia and industry. We will discuss the importance of unifying data processing as well as the current efforts to achieve it. In particular, we will introduce a classification of the different cases where an application needs or benefits from data analytics unification and discuss the challenges in each case. Along with this classification, we will also present current efforts known up to date that aim at unifying data processing, such as Apache Beam and Apache Wayang, and emphasize their differences. We will conclude with open problems and their challenges.