“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
NVIDIAIntermediate
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
Executive brief for stakeholdersDownload Program Brief (PDF)
Enterprise Track
NVIDIA
Accelerated PipelinesDE
cuDF, Dask, NVTabular workflows
NVIDIA Data Engineering
PIPE
On this page
Ideal for
Data EngineerData ScienceTailored Team DeliveryImplementation-Focused
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
- Intermediate Python knowledge including objects and list comprehension.
- Familiarity with pandas.
- Introductory statistics knowledge is helpful.
Program Outcomes
Capabilities your teams will gain
Apply GPU-accelerated techniques to data engineering workflows
Use cuDF, Dask, and NVTabular in pipeline-oriented scenarios
Identify opportunities to reduce bottlenecks in data movement and processing
Build stronger engineering fluency in accelerated data platforms
Curriculum
Curriculum roadmap
1
Accelerated pipeline foundations
2
GPU-enabled transformation workflows
3
Distributed processing patterns with Dask
4
Feature pipeline acceleration with NVTabular
1Module 1
Build accelerated pipeline foundations
+
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
+
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
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryIntermediate practitioner depthBusiness outcome alignment
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
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
