Accelerating Data Engineering Pipelines

Price
$500.00 USD

Duration
1 Day

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

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.

Course Objectives

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 on millions of data points in less than a second.

Who Should Attend?

Experienced Python Developers

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
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Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Course Prerequisites

ntermediate knowledge of Python (list comprehension, objects)
Familiarity with pandas a plus
Introductory statistics (mean, median, mode)

Agenda

Course Introduction
Data on the Hardware Level
ETL with NVTabular
Data Visualization
Final Project: Data Detective

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