So you've learned about the data analytics capabilities of Python, and now you're ready to start churning through data -- great! But do you know how to turn your snippet of code into a system capable of taking in streams of raw sensor data and spitting out insights? This presentation will lay out the basic components of a Python-based data pipeline built for Internet-of-Things (IoT) applications, and will highlight some of the common challenges associated with putting together an efficient data analytics and storage system.
Key topics include:
- An overview of cloud-based "serverless" data pipelines;
- Pros and cons of locally-hosted or "edge computing" systems;
- Tradeoffs between cost, scalability, complexity, and development time for different architectures
By the end of this presentation, you will have gained a broad overview of the ecosystem needed to support a Python analytics solution: how to get data in and out, how to write and deploy scalable code, and how to manage system cost and complexity.