# Data Engineering for HPC with Python

arXiv: https://arxiv.org/abs/2010.06312 · HTML: https://mlsanity.com/paper/2010.06312/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks](https://mlsanity.com/paper/2107.12807/) (2021)
- [What About the Data? A Mapping Study on Data Engineering for AI Systems](https://mlsanity.com/paper/2402.05156/) (2024)
- [HPTMT Parallel Operators for High Performance Data Science & Data Engineering](https://mlsanity.com/paper/2108.06001/) (2021)

## Abstract (arXiv)

Data engineering is becoming an increasingly important part of scientific discoveries with the adoption of deep learning and machine learning. Data engineering deals with a variety of data formats, storage, data extraction, transformation, and data movements. One goal of data engineering is to transform data from original data to vector/matrix/tensor formats accepted by deep learning and machine learning applications. There are many structures such as tables, graphs, and trees to represent data in these data engineering phases. Among them, tables are a versatile and commonly used format to load and process data. In this paper, we present a distributed Python API based on table abstraction for representing and processing data. Unlike existing state-of-the-art data engineering tools written purely in Python, our solution adopts high performance compute kernels in C++, with an in-memory table representation with Cython-based Python bindings. In the core system, we use MPI for distributed memory computations with a data-parallel approach for processing large datasets in HPC clusters.