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Data Engineering for Engineering Data


Large collections of timeseries data power applications like predictive maintenance, digital twin models, AI with signals, and fleet analytics. In this webinar, we explore options and implications for how to efficiently organize and store large timeseries datasets to support downstream applications.

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.


Highlights include:

  • 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 and tall arrays
  • Building AI models with out-of-memory sensor data
  • Accelerating workflows with parallel and cloud computing

About the Presenter

Adam Filion is a Senior Product Marketing Manager at MathWorks where he focuses on building demonstration and teaching materials for the MATLAB platform. You can also find him teaching the Practical Data Science with MATLAB specialization on Coursera and in many other MathWorks videos. He has a BS and MS in Aerospace Engineering from Virginia Tech.

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Data Engineering for Engineering Data

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