Data Engineering for Engineering Data
| Start Time | End Time |
|---|---|
| 16 Apr 2026, 2:00 PM EDT | 16 Apr 2026, 3:00 PM EDT |
Overview
Large collections of time-series sensor data power applications such as predictive maintenance, digital twin models, AI with signals, and fleet analytics. In this webinar, we’ll explore strategies and best practices for efficiently organizing and storing large time-series datasets to enable scalable, downstream analytics and applications.
Agenda
- How data engineering enables scalable, iterative analysis workflows
- Preparing engineering data with ETL for analytics
- Organizing time-series data for “for-each” and “across-all” analysis
- Improving performance with engineered formats and metadata-aware access
- Example workflow: Engineering and analyzing NASA aircraft flight data in MATLAB
Highlights
- Accessing raw data from different files and sources
- Organizing data utilizing different table schemas
- Storing data with the Parquet file format
- Analyzing large datasets with datastores
- Building AI models with out-of-memory sensor data
- Accelerating workflows with parallel and cloud computing
Who Should Attend
This seminar is intended for engineers, data scientists, and data engineers who work with large-scale engineering or time-series data and want to enable efficient, iterative analysis using MATLAB.
About the Presenter
Mil Shastri serves as a principal application engineer at The MathWorks, specializing in the application of data analytics technologies in industrial automation, energy, and utilities sectors. Leveraging over 20 years of hands-on experience, Mil works with customers in implementing machine learning algorithms and enterprise-level data analytics solutions that drive operational efficiencies. With a background in both quantitative trading algorithms and industrial automation, he offers a unique perspective. Mil has an M.S. from the University of Florida and a B.Tech from IIT Delhi in Mechanical Engineering. As an industry thought leader, he is eager to share insights on the intersection of big data, AI, and industrial applications
Product Focus
We will not sell or rent your personal contact information. See our privacy policy for details.
You are already signed in to your MathWorks Account. Please press the "Submit" button to complete the process.
