A Python Workshop To Efficiently Access, Deliver, Visualize, and Analyze Big Environmental Data in the Cloud
Ryan Paul Lafler
Tuesday, January 13, 2026
10:00 am - 2:00 pm PT
Virtual 4-Hour Workshop (Half-Day)
$35.95
Workshop Tags
Python, Big Data, Cloud Computing, Environmental Data, Amazon S3, Google Cloud Storage, Dask, Xarray, fsspec, Climate Data, Meteorology, NetCDF, GRIB2, Certificate, CMIP6, NOAA's RTMA
Workshop Description
This 4-hour, hands-on virtual workshop presented by Ryan Paul Lafler provides a practical introduction to accessing, processing, and analyzing big environmental datasets directly in the cloud using Python. Designed for environmental data scientists, climate researchers, programmers, and analysts, this course focuses on efficient, scalable strategies for connecting to and working with petabyte-scale data stored in Amazon S3, Google Cloud Storage (GCS), and Microsoft Azure without the need to download entire repositories or duplicate files locally.
Participants will build Python-based data access and processing pipelines capable of retrieving only the information they need, reducing memory usage and improving performance. Using modern open-source libraries, including Dask for scalable computation, Xarray for multidimensional spatiotemporal data, and fsspec for connecting to cloud object storage, attendees will learn how to efficiently process, visualize, and analyze massive climatological and meteorological datasets directly from the cloud.
Key topics include:
Connecting to cloud object storage (Amazon S3, GCS, Azure) using Python and fsspec
Working with real-world datasets including the Coupled Model Intercomparison Project (CMIP6) and NOAA's Real-Time Mesoscale Analysis (RTMA)
Understanding NetCDF, GRIB2, and HDF5 data structures
Efficient data access using lazy loading, chunking, and graph-based execution
Building scalable data pipelines with Dask and Xarray
Extracting time series and performing spatiotemporal analysis
Visualizing environmental data using Matplotlib, Cartopy, and Rasterio
All participants will receive a Certificate of Completion, non-redistributable PDF slides, a documented Jupyter Notebook, and cloud data access examples to continue practicing independently.
Workshop Summary
Unlock the power of Python to access, process, and visualize petabytes of environmental data in the cloud. In this 4-hour hands-on workshop, you’ll connect to real-world climatological and meteorological datasets, including the petabytes-large Coupled Model Intercomparison Project (CMIP6) and NOAA's hourly Real-Time Mesoscale Analysis (RTMA) data, stored in Amazon S3 and Google Cloud Storage. Learn to efficiently retrieve and analyze data using Dask, Xarray, and fsspec, all without downloading or storing massive files locally. From lazy loading and data chunking to spatiotemporal analysis and visualization, you’ll master the tools and workflows needed to work with big environmental data at scale.
Benefits of Enrolling in this Workshop
By enrolling in this workshop, attendees will:
Earn a Certificate of Completion from Premier Training by Premier Analytics Consulting
Learn to connect Python applications directly to cloud data sources (Amazon S3, GCS, Azure)
Build efficient pipelines for retrieving and processing large climatological and meteorological datasets
Understand and apply big data techniques including lazy loading, chunking, and parallel computing
Use Dask, Xarray, fsspec, and visualization tools like Cartopy and Matplotlib for scalable analysis
Gain hands-on experience working with real, research-grade environmental data
Intended Audience
Intermediate. Ideal for data scientists, researchers, programmers, students, professors, and professionals interested in cloud computing and large-scale environmental data analysis using Python on their local systems.
Prerequisites
Basic familiarity with Python programming and working in Jupyter Notebook.
Prior exposure to data analysis or environmental datasets is helpful but not required.
All key tools and workflows will be introduced during the workshop.
About the Trainer
Ryan Paul Lafler is the President, CEO, and Lead Consultant of Premier Analytics Consulting, LLC, a San Diego–based firm specializing in AI/ML solutions, applied data science, big data processing, and full-stack systems development. He partners with clients across private industry, research, and government to design scalable, open-source workflows that power big-data pipelines, custom-built full-stack systems, agentic and generative AI copilots and processes, and advanced analytics applications for real-world decision support.
Ryan brings extensive experience as a consultant, big data scientist, AI engineer, full-stack developer, and statistician. His expertise spans Python, R, SAS®, SQL, and modern JavaScript frameworks (React, Node.js, Vite), alongside applied AI/ML, deep learning, databases, and statistical software.
He holds an M.S. in Big Data Analytics (2023) and a B.S. in Statistics (2020) from San Diego State University, where he also serves as Adjunct Faculty in the Big Data Analytics Graduate Program, the Department of Mathematics and Statistics, and the Global Campus program.