“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
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.
Duration
8 hours
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
NVIDIA
Accelerated PipelinescuDF, Dask, NVTabular workflows
NVIDIA Data Engineering
On this page
Ideal for
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Accelerating Data Engineering Pipelines as an MCT-led NVIDIA program for Data Scientist. Typical duration is 8 hours (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Built for practitioners who need to improve data engineering pipeline performance with GPU-enabled tools and workflow techniques.
Overview
Executive overview
Official NVIDIA DLI program exploring advanced tools and techniques for GPU-accelerated data engineering pipelines.
Readiness
Prerequisites
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in data engineering scenarios
Strengthen capability in nvidia rapids scenarios
Strengthen capability in data science scenarios
Curriculum
Curriculum roadmap
Data Engineering
NVIDIA RAPIDS
Data Science
1Module 1
Data Engineering
+
Module 1
Data Engineering
Cover data engineering skills and implementation practices aligned to Accelerating Data Engineering Pipelines.
- Cover data engineering skills
- implementation practices aligned to Accelerating Data Engineering Pipelines
2Module 2
NVIDIA RAPIDS
+
Module 2
NVIDIA RAPIDS
Cover nvidia rapids skills and implementation practices aligned to Accelerating Data Engineering Pipelines.
- Cover nvidia rapids skills
- implementation practices aligned to Accelerating Data Engineering Pipelines
3Module 3
Data Science
+
Module 3
Data Science
Cover data science skills and implementation practices aligned to Accelerating Data Engineering Pipelines.
- Cover data science skills
- implementation practices aligned to Accelerating Data Engineering Pipelines
Delivery Models
Delivery models
Engagement Fit
Engagement fit
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Supports faster and more efficient data pipeline execution by helping teams apply NVIDIA GPU-accelerated engineering techniques to modern data workflows.
- •Map exercises to your ingestion and transformation pipeline patterns
- •Add medallion architecture or feature-engineering emphasis
- •Extend into RAPIDS and Spark acceleration adoption discussions
Credentials
Certification & official source
- •NVIDIA DLI Certificate of Competency
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
