Unified Data Analytics: State-of-the-art and Open Problems

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Abstract

There is an urgent need for unifying data analytics as more and more applications require diverse data analytic tasks: 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 analytics 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 the current efforts known up to date to unify data analytics.