This document discusses using logs to gain insights through analysis. It presents an overview of building a log processing stack including collecting real-time and batch data, storing and processing it, and visualizing the results. The goal is to use logs to enhance customer experience and support like providing more personalized service based on interactions.

2. Partnership

3. Why should
you miming logs?

4. New services
Productivity
New indicators

5. What really is
a log?

6. Stream
Incredible event-data sources

7. Heart-beat
error stacks and business events

8. Trivial
a log is not complex*

9. Cheap
already in place and separated

10. Structure-less
java stacks, events, text, and so on

11. Volume
logs can be very verbose

12. Need processing
I hope, no one is fluency

13. How build a
stack for this?

14. user experience
intuitive, simple, easy
processing
enrichment, algorithms
data
m2m, marketing, web

16. Pipeline
#Real-time
QMS, API, Stream
#Batch
ETL, Dump, flat files

17. Real-time
Batch
Flume/Storm
Sqoop/Files

18. Storage
#Long-term
Analytics, data processing
#(No)SQL
Flexibility, scalability, API

19. Batch
NoSQL
Elasticsearch/HBase
Long-term storage
HDFS/Hadoop

20. Process
Algorithms, Datamiming,
Datascience
Sandbox
Ad-Hoc Analysis
Data aggregation

21. Batch API
Processing
M/R technology
Pig, Hive, Impala
API/JDBC
Cascalog, Java

22. Visualize
"Probably the most important thing"

23. Real-time
Batch API
Processing
https
Dev HTML5
AngularJS, D3JS
Reporting
Tableau, Qlikview

24. Real-time
Batch API
Processing
https

25. Hosteam
"use case"

26. Hosteam email plateform

29. Productivity
Better support
quality and reactivity
Premium
clients based
on their interactions
Individual
counters for each
customers

30. Groupe Cyrès
87 avenue du Maine
75014 Paris
Tél. : +331 72 50 01 26
Guillaume Polaert (gpolaert@cyres.fr)