🌟 Session Overview 🌟
Session Name: Supernovas, Black Holes, and Streaming Data: A Journey in Space with Apache Kafka data streams from NASA Speaker: Frank Munz Session Description: In this fun, hands-on, and in-depth How-To, we explore NASA's GCN project, which publishes various events in space as Kafka topics.
The focus of my talk is on end-to-end data engineering, from consuming the data and ELT-ing the stream, to using generative AI tools for analytics.
We will analyze GCN data in real time, specifically targeting the data stream from exploding supernovas. This data triggers dozens of terrestrial telescopes to potentially reposition and point toward the event.
The speaker will kick off the session by contrasting various ways of ingesting and transforming the data, discussing their trade-offs: Should you use a declarative data pipeline, or can a data analyst manage with SQL only? Alternatively, when would it be better to follow the classic approach of orchestrating Spark notebooks to get the data ingested?
He will answer the question: Does a data engineer working with streaming data benefit from generative AI-based tools and assistants today? Is it worth it, or is it just hype?
The demo is easy to replicate at home, and Frank will share the notebooks in a GitHub repository so you can analyze real NASA data yourself!
This session is ideal for data engineers, data architects who enjoy some coding, generative AI enthusiasts, or anyone fascinated by technology and the sparkling stars in the night sky.
While the focus is clearly on tech, the demo will run on the open-source and open-standards-based Databricks Intelligence Platform (so inevitably, you'll get a high-level overview here too).
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