Nvidia Accelerating Data Engineering Pipelines (ADEP)
Ziele der Schulung
Explore how to employ advanced data engineering tools and techniques with GPUs to significantly improve data engineering pipelines.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
- 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.
Zielgruppe Seminar
This course is designed for data engineers, data scientists, and software developers who want to optimize data processing workflows and improve performance using GPU acceleration. It is ideal for professionals working with large-scale data pipelines, ETL processes, and real-time data visualization in fields such as big data analytics, finance, AI, and high-performance computing (HPC). Participants should have experience with Python-based data frameworks like Pandas and an interest in accelerating data manipulation and visualization.
Voraussetzungen
- Intermediate knowledge of Python (list comprehension, objects)
- Familiarity with pandas a plus
- Introductory statistics (mean, median, mode)
Lernmethodik
Die Schulung bietet Ihnen eine ausgewogene Mischung aus Theorie und Praxis in einer erstklassigen Lernumgebung. Profitieren Sie vom direkten Austausch mit unseren projekterfahrenen Trainern und anderen Teilnehmern, um Ihren Lernerfolg zu maximieren.
Seminarinhalt
Data on the Hardware Level
- Explore the strengths and weaknesses of different hardware approaches to data and the frameworks that support them:
- Pandas
- CuDF
- Dask
ETL with NVTabular
- Learn how to scale an ETL pipeline from 1 GPU to many with NVTabular through the perspective of a big data recommender system.
- Transform raw json into analysis-ready parquet files
- Learn how to quickly add features to a dataset, such as Categorify and Lambda operators
Data Visualization
- Step into the shoes of a meteorologist and learn how to plot precipitation data on a map.
- Learn how to use descriptive statistics and plots like histograms in order to assess data quality
- Learn effective memory usage, so users can quickly filter data through a graphical interface
Final Project: Data Detective
- Users are complaining that the dashboard is too slow. Apply the techniques learned in class to find and eliminate efficiencies in the backend code
Final Review
- Review key learnings and answer questions.
- Complete the assessment and earn your certificate.
- Complete the workshop survey.
- Learn how to set up your own AI application development environment.
Hinweise
Partner
Dieses Seminar bieten wir in Kooperation mit unserem Nvidia Learning Partner Fast Lane Institute for Knowledge Transfer GmbH an
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Übersicht: NVIDIA Schulungen Portfolio
