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L’analyse en temps réel
de Big Data
 Le monitoring de flux
par l’exemple
Abdellatif BOUCHAMA
@a_bouchama
Abdellatif BOUCHAMA
@A_BOUCHAMA
• Middleware architect, and
passionate about the new
technology: Big Data,IoT and
Open sou...
Agenda
Big Data in 2015:Trends & Statistics
The emergence of real time
Use Case: Flow Activity Monitoring
Demo
What is Big Data?
Big Data
Velocity
30KB/s --> 30GB/s
· Batch
· Real-Time
· Streaming
Volume
1-2 Terabytes  ∞
Variety
· S...
Big Data Expectations:
Things That Excite Executives About Big Data
It’s the “next oil.” from the Ginni Rometty, CEO of IB...
Big Data view by Gartner:
Big Data in 2015:
75% of Companies Are Investing or Planning to Invest in Big Data in the NextTwoYears
Goals for Big Data ...
The emergence of Real-Time
Low
Pure Batch
Operationalizing
Near RealTime
Or
Interactive
Analytics
High
RealTime
Analytics
...
Use case: Flow Activity
Monitoring
Architecture
Constraints & Requirements
Constraints
• Non intrusive system
• No modification on business flows.
• We can plug it and un...
Why Elasticsearch stack?
Open source
Easy to deploy
Distributed
Linear scalability
REST Interface
Kibana & Logstash to com...
Data flow and constraints
Collect
JMX
Store & transport
Transform & Access
Modelize & Analyze
Visualize & Predict
JMS
DemoTime!
Be prepared for it to fail, because demos always do
What’s Next ?
• Enrichment analysis, after adding business
information in the logs
• Integration of backend and frontend
a...
Thank you
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Analyse en temps réel de BigData

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Tous les projets stratégiques ont besoin de gestion et de surveillance, et les questions qui se posent souvent:

1. Comment puis-je visualiser la santé de ma plate-forme?
2. Comment puis-je analyser les données de mes applications? 3. Comment puis-je visualiser la performance de l'entreprise et la part des objectifs atteints?

Dans cette présentation nous allons voir comment nous pouvons répondre à ces questions et donner des tableaux de bord spécifiques fondés sur un ensemble de produits open source qui permettent de centraliser et d'analyser des données de votre SI en temps réel.

Published in: Data & Analytics
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Analyse en temps réel de BigData

  1. 1. L’analyse en temps réel de Big Data  Le monitoring de flux par l’exemple Abdellatif BOUCHAMA @a_bouchama
  2. 2. Abdellatif BOUCHAMA @A_BOUCHAMA • Middleware architect, and passionate about the new technology: Big Data,IoT and Open source • Co-founder of BusHorn.com
  3. 3. Agenda Big Data in 2015:Trends & Statistics The emergence of real time Use Case: Flow Activity Monitoring Demo
  4. 4. What is Big Data? Big Data Velocity 30KB/s --> 30GB/s · Batch · Real-Time · Streaming Volume 1-2 Terabytes  ∞ Variety · Structured · Semi-Structured · UnStructured
  5. 5. Big Data Expectations: Things That Excite Executives About Big Data It’s the “next oil.” from the Ginni Rometty, CEO of IBM The potential to revolutionize industries, and change business models (e.g., Uber and Airbnb), with what we learn from the data. The ability to provide real-time graphing solutions for data relationships. Real-time operational and business data allows people to make decisions faster thereby saving significant money. The opportunities it gives us to help clients solve real business problems. https://dzone.com/articles/14-things-that-excite-executives-about-big-data?oid=big_data
  6. 6. Big Data view by Gartner:
  7. 7. Big Data in 2015: 75% of Companies Are Investing or Planning to Invest in Big Data in the NextTwoYears Goals for Big Data initiatives: 1. Enhancing the customer experience 2.Streamlining existing processes 3.Achieving more targeted marketing and reducing costs Last year, 37% of big data projects were initiated by the CIO, while 25% were initiated by business unit heads. In 2015, the roles are nearly tied, at 32% and 31 %, respectively. http://www.gartner.com/newsroom/id/3130817
  8. 8. The emergence of Real-Time Low Pure Batch Operationalizing Near RealTime Or Interactive Analytics High RealTime Analytics • Right timeReal time • Smart dataBig Data
  9. 9. Use case: Flow Activity Monitoring
  10. 10. Architecture
  11. 11. Constraints & Requirements Constraints • Non intrusive system • No modification on business flows. • We can plug it and unplug it easily. Requirements • System cost should be mastered and adaptable • System automatized • Measurement
  12. 12. Why Elasticsearch stack? Open source Easy to deploy Distributed Linear scalability REST Interface Kibana & Logstash to complete the Landscape
  13. 13. Data flow and constraints Collect JMX Store & transport Transform & Access Modelize & Analyze Visualize & Predict JMS
  14. 14. DemoTime! Be prepared for it to fail, because demos always do
  15. 15. What’s Next ? • Enrichment analysis, after adding business information in the logs • Integration of backend and frontend applications • Audit
  16. 16. Thank you

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