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@gamussa @confluentinc @thephillyjug Divide, Distribute and Conquer:
 Stream v. Batch

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Stream v. Batch

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Who am I?

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Solutions Architect Who am I?

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Solutions Architect Developer Advocate Who am I?

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Solutions Architect Developer Advocate @gamussa in internetz Who am I?

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Solutions Architect Developer Advocate @gamussa in internetz Hey you, yes, you, go follow me in twitter © Who am I?

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@gamussa @confluentinc @thephillyjug Disclaimer:
 


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@gamussa @confluentinc @thephillyjug BATCH PROCESSING Data at rest

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@gamussa @confluentinc @thephillyjug Data and Queries Origin and processing

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@gamussa @confluentinc @thephillyjug

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@gamussa @confluentinc @thephillyjug Data…

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@gamussa @confluentinc @thephillyjug Data…

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@gamussa @confluentinc @thephillyjug ✓ … inherently immutable Data… ✓ … time-based

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@gamussa @confluentinc @thephillyjug CRUD -> CR

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@gamussa @confluentinc @thephillyjug Processing is a query

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@gamussa @confluentinc @thephillyjug Processing is a query Function on full data set

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@gamussa @confluentinc @thephillyjug Processing is a query Function on full data set Projection

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@gamussa @confluentinc @thephillyjug Processing is a query Function on full data set Projection Aggregations

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@gamussa @confluentinc @thephillyjug Processing is a query Function on full data set Projection Aggregations Joins

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SELECT user_vote, count(*) FROM AccessLog WHERE event_date BETWEEN"04/07/2017" AND "04/07/2017" GROUP BY user_vote;

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SELECT user_vote, count(*) FROM AccessLog WHERE event_date BETWEEN "04/7/2017" AND "04/08/2017" GROUP BY user_vote;

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SELECT user_vote, count(*) FROM AccessLog WHERE event_date BETWEEN"04/07/2017" AND "04/08/2007" GROUP BY user_vote;

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@gamussa @confluentinc @thephillyjug Lambda architecture origins http:/ /nathanmarz.com/blog/how-to-beat-the-cap-theorem.html

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@gamussa @confluentinc @thephillyjug Lambda Architecture

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@gamussa @confluentinc @thephillyjug TFW Trying to explain modern big data landscape

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@gamussa @confluentinc @thephillyjug Precomputed Results http:/ /nathanmarz.com/blog/how-to-beat-the-cap-theorem.html

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@gamussa @confluentinc @thephillyjug Batch Process http:/ /nathanmarz.com/blog/how-to-beat-the-cap-theorem.html

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@gamussa @confluentinc @thephillyjug STREAM PROCESSING Data is motion

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@gamussa @confluentinc @thephillyjug Streaming Platform

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@gamussa @confluentinc @thephillyjug Streaming Platform

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@gamussa @confluentinc @thephillyjug Directed Acyclic Graph

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@gamussa @confluentinc @thephillyjug DEMO

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@gamussa @confluentinc @thephillyjug DEMO

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@gamussa @confluentinc @thephillyjug Interesting cases Before You Go

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I FOUND YOUR LACK OF FAULT TOLERANCE DISTURBING

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Data is too important to store it in one computer

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@gamussa @confluentinc @thephillyjug How to process «infinite» data?

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@gamussa @confluentinc @thephillyjug Time model

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@gamussa @confluentinc @thephillyjug Time model Different use cases time semantics

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@gamussa @confluentinc @thephillyjug Time model Different use cases time semantics Majority of use cases require event- time semantics

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@gamussa @confluentinc @thephillyjug Time model Different use cases time semantics Majority of use cases require event- time semantics Other use cases may require processing-time or special variants like ingestion-time

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@gamussa @confluentinc @thephillyjug Time Model

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@gamussa @confluentinc @thephillyjug Time Model

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@gamussa @confluentinc @thephillyjug Time Model

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Finite Representation Of Infinite Data

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@gamussa @confluentinc @thephillyjug Windowing Windowing is an operation that groups events

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@gamussa @confluentinc @thephillyjug https:/ /www.oreilly.com/ideas/the-world-beyond-batch-streaming-101

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@gamussa @confluentinc @thephillyjug Windowing Input data, where colors represent
 different users events Rectangles denote
 different event-time
 windows processing-time event-time windowing alice bob dave

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@gamussa @confluentinc @thephillyjug Windowing Windowing is an operation that groups events Most commonly needed: time windows, session windows Examples: ✗Real-time monitoring: 5-minute averages ✗Reader behavior on a website: user browsing sessions

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@gamussa @confluentinc @thephillyjug Fatality

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@gamussa @confluentinc @thephillyjug Out-of-order and late data Is very common in practice, not a rare corner case ✗Related to time model discussion

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@gamussa @confluentinc @thephillyjug Out-of-order and late data

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@gamussa @confluentinc @thephillyjug Out-of-order and late data Users with mobile phones enter
 airplane, lose Internet connectivity

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@gamussa @confluentinc @thephillyjug Out-of-order and late data Users with mobile phones enter
 airplane, lose Internet connectivity Emails are being written
 during the 10h flight

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@gamussa @confluentinc @thephillyjug Out-of-order and late data Users with mobile phones enter
 airplane, lose Internet connectivity Emails are being written
 during the 10h flight Internet connectivity is restored,
 phones will send queued emails now

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@gamussa @confluentinc @thephillyjug Stream Processing: results

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@gamussa @confluentinc @thephillyjug Stream Processing: results • Yes, it’s possible to get computation results in real time

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@gamussa @confluentinc @thephillyjug Stream Processing: results • Yes, it’s possible to get computation results in real time • Windows – finite view of infinite data • Based on temporal characteristics of the evet

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@gamussa @confluentinc @thephillyjug Stream Processing: results • Yes, it’s possible to get computation results in real time • Windows – finite view of infinite data • Based on temporal characteristics of the evet • Late event processing • You choose how long to wait

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@gamussa @confluentinc @thephillyjug https://github.com/confluentinc/kafka-streams-examples

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@gamussa @confluentinc @thephillyjug Thanks! questions? @gamussa viktor@confluent.io