Bytetalks ep3: Build Streaming Pipelines in Python with Bytewax
Bytewax Cheatsheet: Your go-to guide for mastering data flows and improving streaming analytics with Bytewax
Written by Zander Matheson & Laura Funderburk
Welcome to π Bytetalks, your new go-to series for all things Bytewax and streaming data!
In this episode, Laura Funderburk and Zander Matheson explore Bytewax operators and their role in transforming streaming data. We cover essential Bytewax concepts, from building data flows as directed graphs to understanding how operators funΡction as nodes that modify data in real time.
Key topics include:
- Differences between stateless and stateful operators and why state matters.
- Live coding examples using smoothie orders to showcase filtering, enrichment, and state management.
- Techniques like keying data for aggregation and using caching to improve efficiency.
Zander also explains why managing state and caching are critical for efficient data processing in Bytewax.
P.S. If you missed the previous episodes, no worries! Check out the links below to catch up and get all the insights you need!
π Bytetalks ep.1: Real-Time Analytics with Bytewax & ClickHouse
π Bytetalks ep.2: Real-Time Embeddings with Azure AI & Bytewax
π Bytetalks ep.3: Build Streaming Pipelines in Python with Bytewax
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Zander Matheson
CEO, FounderZander is a seasoned data engineer who has founded and currently helms Bytewax. Zander has worked in the data space since 2014 at Heroku, GitHub, and an NLP startup. Before that, he attended business school at the UT Austin and HEC Paris in Europe.
Laura Funderburk
Senior Developer AdvocateLaura Funderburk holds a B.Sc. in Mathematics from Simon Fraser University and has extensive work experience as a data scientist. She is passionate about leveraging open source for MLOps and DataOps and is dedicated to outreach and education.
Bytetalks ep3: Build Streaming Pipelines in Python with Bytewax
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