Data Manipulation at Scale: Systems and Algorithms

Data analysis has taken over data acquisition as the bottleneck for evidence-based decision making. We are drowning in it. It takes a lot of computing power to extract knowledge from noisy, heterogeneous and large datasets. You also need the right programming abstractions in order to do so effectively. In the past decade, abstractions have been created that combine ideas from distributed systems, parallel databases, and programming languages to create new scalable data analytics platforms. These platforms are the foundation of data science at realistic scales.