Data centers are increasingly adopting renewable energy sources to mitigate environmental impact and reduce operational costs. However, effectively optimizing energy costs remains challenging due to unpredictable workloads and fluctuating renewable energy availability. This paper introduces LOECM, a Lyapunov-driven online scheduling algorithm designed to minimize energy cost without relying on future information. LOECM is formulated as an online stochastic optimization problem and leverages Lyapunov optimization techniques to balance immediate cost minimization against task queue stability. By exploiting the inherent structure of the optimization problem, we design an efficient solver with constant time and space complexity ($\mathcal{O}(1)$). Additionally, we propose the LOECM scheduling policy to dynamically manage workloads and node activation based on a covering subset strategy. To evaluate the practicality and effectiveness of our approach, we implement a prototype system called LOECMHadoop, integrating the LOECM scheduler into Hadoop-a widely-used data processing platform-across a 10- node cluster. Experimental results show that our power model has an average deviation of approximately 5%, and LOECM achieves over 20% electricity cost savings compared without our policy on the prototype system.

Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA tasks for statistical modeling. Their API design is either too low level, which is optimized for plotting rather than EDA, or too high level, which is hard to specify more fine-grained EDA tasks. In response, we propose DataPrep.EDA, a novel task-centric EDA system in Python. DataPrep.EDA allows data scientists to declaratively specify a wide range of EDA tasks in different granularity with a single function call. We identify a number of challenges to implement DataPrep.EDA, and propose effective solutions to improve the scalability, usability, customizability of the system. In particular, we discuss some lessons learned from using Dask to build the data processing pipelines for EDA tasks and describe our approaches to accelerate the pipelines. We conduct extensive experiments to compare DataPrep.EDA with Pandas-profiling, the state-of-the-art EDA system in Python. The experiments show that DataPrep.EDA significantly outperforms Pandas-profiling in terms of both speed and user experience. DataPrep.EDA is open-sourced as an EDA component of DataPrep: https://github.com/sfu-db/dataprep.