Program Outline
Accelerating Data Engineering Pipelines
This NVIDIA DLI program explores how to employ advanced data engineering tools and techniques with GPUs to improve pipeline performance. It is well suited for teams that want stronger pipeline efficiency before scaling more complex data and AI workloads.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
NVIDIA RAPIDS, NVTabular
Role
Data Engineer
NVIDIA
Accelerated PipelinescuDF, Dask, NVTabular workflows
NVIDIA Data Engineering
Best Fit
Audience Profile
Who This Program Is For
Built for practitioners who need to improve data engineering pipeline performance with GPU-enabled tools and workflow techniques.
Overview
Program Summary
Official NVIDIA DLI program exploring advanced tools and techniques for GPU-accelerated data engineering pipelines.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Build accelerated pipeline foundations
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Module 1
Build accelerated pipeline foundations
Explore the core tools and patterns needed to use GPUs effectively within data engineering and transformation workflows.
- Accelerated pipeline foundations
- GPU-enabled transformation workflows
2Module 2
Scale engineering workflows for higher throughput
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Module 2
Scale engineering workflows for higher throughput
Apply distributed and feature-oriented workflow techniques to improve pipeline reliability and throughput on larger data volumes.
- Distributed processing patterns with Dask
- Feature pipeline acceleration with NVTabular
Coverage Areas
Topic Coverage
Coverage Item 1
Accelerated pipeline foundations
Coverage Item 2
GPU-enabled transformation workflows
Coverage Item 3
Distributed processing patterns with Dask
Coverage Item 4
Feature pipeline acceleration with NVTabular
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Map exercises to your ingestion and transformation pipeline patterns
- •Add medallion architecture or feature-engineering emphasis
- •Extend into RAPIDS and Spark acceleration adoption discussions
