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A Microservice Architecture for Big Data PipelinesBigData.be Meetup June 2016
Let’s face it: Big Data is no longer a Big Deal
2Image © User:Kleiner / Wikimedia Commons / CC BY-SA 3.0
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Yardsticks of Software Development:
1. Create Modularity
2. Ensure Quality
3. Scale Development
4. Painless Deployment
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
Image © User:Guma89 / Wikimedia Commons / CC BY-SA 3.0
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1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
Modularity is Imperative for On Premise Deployments:
RealImpact
Product Product Product
Client Client Client
The Promised Land
5Image http://hanciong.deviantart.com/art/old-world-map-253195357
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Micro Services: Maximal Modularity
1. No shared state
2. Minimal coupling
3. Separation of concerns
4. Mix & match
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
www.realimpactanalytics.com
Micro Services: Scalable Development
1. Team responsibility
2. Less code = faster ramp up
3. Technology independence
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
www.realimpactanalytics.com
Micro Services: Painless Deployment
1. Reproducible environments
2. Versioned APIs
3. Installation = docker-compose up
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
Prod
Dev
www.realimpactanalytics.com
Micro Services: QA Friendly
1. Three levels of testing
• Class / function level
• Service level
• Integration level
2. Staging is no big deal
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
www.realimpactanalytics.com
Translation to Big Data Pipelines…
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
TrendingAnalysis
Twitter Data
TopTweeters
Recommend
www.realimpactanalytics.comcontainer
Translation to Big Data Pipelines…
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
TrendingAnalysis
manifest.yaml
run.sh
jar
runtime
www.realimpactanalytics.comcontainer
Translation to Big Data Pipelines…
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
TrendingAnalysis
datasources: - twitter
outputs: - id: daily-trends fields: - name: keyword type: string - name: relevance type: integer
parameters: …
manifest.yaml
run.sh
jar
runtime
www.realimpactanalytics.com
Translation to Big Data Pipelines…
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
TrendingAnalysis
HDFS
Input Data
Result
Parameters
Demo
14
www.realimpactanalytics.com
Data Modules: QA Friendly?
1. Three levels of testing ✔
• Class / function level
• Module level
• Integration level
2. Staging is no big deal ✔
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
www.realimpactanalytics.com
Data Modules: Painless Deployment?
1. Reproducible environments (✔)
2. Versioned APIs ✔
3. Installation = docker-compose up (✔)
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
Prod
Dev
www.realimpactanalytics.com
Data Modules: Scalable Development?
1. Team responsibility ✔
2. Less code = faster ramp up ✔
3. Technology independence ✔
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
www.realimpactanalytics.com
Data Modules: Modularity?
1. No shared state (well…)
2. Minimal coupling ✔
3. Separation of concerns ✔
4. Mix & match ✔
1. Challenge: Big Data in Production
2. Zen of Micro Services
3. Data Modules
4. Conclusion
Conclusion
19
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