Nvidia DLI – Accelerating Data Engineering Pipelines


Nvidia DLI - Accelerating Data Engineering Pipelines


Dr. Laszlo, Kovacs

Content and organization

  • Data engineering is the foundation of data science and lays the groundwork for analysis and modeling. In order for organizations to extract knowledge and insights from structured and unstructured data, fast access to accurate and complete datasets is critical. Working with massive amounts of data from disparate sources requires complex infrastructure and expertise. Minor inefficiencies can result in major costs, both in terms of time and money, when scaled across millions to trillions of data points. In this workshop, we’ll explore how GPUs can improve data pipelines and how using advanced data engineering tools and techniques can result in significant performance acceleration. Faster pipelines produce fresher dashboards and machine learning (ML) models, so users can have the most current information at their fingertips.

By participating in this workshop, you’ll learn:

  • how data moves within a computer. How to build the right balance between CPU, DRAM, Disk Memory, and GPUs,
  • how different file formats can be read and manipulated by hardware,
  • how to scale an ETL pipeline with multiple GPUs using NVTabular,
  • how to build an interactive Plotly dashboard where users can filter millions of data points in less than a second.



Course Duration

8 hours

Course Type

Short Course

Participation terms

Free of charge for university students and staff. Intermediate knowledge of Python (list comprehension, objects), Familiarity with pandas, Introductory statistics (mean, median, mode).

Lecture Plan

Data on the Hardware Level (60 mins), ETL with NVTabular (120), Data Visualization (120 mins), Final Project: Data Detective (60 mins)


01.13.2023 9:00– 01.13.2023 17:00 CET



Modality (online/in person):



Upon successful completion of the assessment, the participant will receive an Nvidia Certificate of Competency.

Host Institution
Nvidia Deep Learning Institute, Faculty of Informatics, University of Debrecen, Hungary

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