Accelerating Data Engineering Pipelines

Accelerating Data Engineering PipelinesADEPNVNvidiaNV-ADEP1.0<ul> <li>How data moves within a computer. How to build the right balance between CPU, DRAM, Disk Memory, and GPUs.</li><li>How different file formats can be read and manipulated by hardware.</li><li>How to scale an ETL pipeline with multiple GPUs using NVTabular.</li><li>How to build an interactive Plotly dashboard where users can filter on millions of data points in less than a second.</li></ul><ul> <li>Intermediate knowledge of Python (list comprehension, objects)</li><li>Familiarity with pandas a plus</li><li>Introductory statistics (mean, median, mode)</li></ul>Introduction <ul> <li>Meet the instructor.</li><li>Create an account at courses.nvidia.com/join</li></ul>Data on the Hardware Level <ul> <li>Explore the strengths and weaknesses of different hardware approaches to data and the frameworks that support them:<ul> <li>Pandas</li><li>CuDF</li><li>Dask</li></ul></li></ul>ETL with NVTabular <ul> <li>Learn how to scale an ETL pipeline from 1 GPU to many with NVTabular through the perspective of a big data recommender system.<ul> <li>Transform raw json into analysis-ready parquet files</li><li>Learn how to quickly add features to a dataset, such as Categorify and Lambda operators</li></ul></li></ul>Data Visualization <ul> <li>Step into the shoes of a meteorologist and learn how to plot precipitation data on a map.</li><li>Learn how to use descriptive statistics and plots like histograms in order to assess data quality</li><li>Learn effective memory usage, so users can quickly filter data through a graphical interface</li></ul>Final Project: Data Detective <ul> <li>Users are complaining that the dashboard is too slow. Apply the techniques learned in class to find and eliminate efficiencies in the backend code</li></ul>Final Review <ul> <li>Review key learnings and answer questions.</li><li>Complete the assessment and earn your certificate.</li><li>Complete the workshop survey.</li><li>Learn how to set up your own AI application development environment.</li></ul>- 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.- Intermediate knowledge of Python (list comprehension, objects) - Familiarity with pandas a plus - Introductory statistics (mean, median, mode)Introduction - Meet the instructor. - Create an account at courses.nvidia.com/join 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.1 jour500.00500.00500.00500.00500.00420.00500.00690.00