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1
Introduction
2
Steps to improve user experience
3
History of data processing at Google
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What is MapReduce
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The problem with MapReduce
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From Java
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The problem
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Artificial splitting
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Un unbounded data
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Delays
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How to deal with delays
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MillVia
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Timebased windows
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Session windows
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Event vs processing time
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Stream vs Batch
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Billing Pipeline
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User Experience
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Abuse Detection
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Historical Systems
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Apache Beam
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Dataflow Example
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Four Questions
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MapReduce
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When to omit results
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Create a window
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Wait for results
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When to trigger
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Triggers in Beam
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Demo
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refinements
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how
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what just happened
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cancel pipeline
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run on
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update pipeline
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QR code
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Assign color
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Running the pipeline
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Patch pipeline
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BigQuery
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Color Smash
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Hit Ratio
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Aggregate
45
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Description:
Explore a comprehensive talk on improving app user experience through unified batch and stream processing using Google's Dataflow model. Dive into the evolution of big data processing, from MapReduce to Apache Beam, and learn how to effectively manage and visualize data streams. Discover practical insights on implementing time-based windows, handling delays, and utilizing triggers in Beam. Follow along with a real-time demonstration showcasing Dataflow's capabilities, including pipeline creation, refinement, and visualization techniques. Gain valuable knowledge on integrating batch and streaming data processing to enhance your application's performance and user experience.

Improve Your App UX with One Model Processing Insights from Batch and Streams

Devoxx
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