2. Agenda & Intervenants

3. Introduction

4. Alter Way in 2 slides

5. Alter Way in 2 slides

6. Elasticsearch in 1 slide
• More than 11 million downloads
• 650,000 New Downloads per Month
• 1000s of Mission Critical Implementations
• Top Investors: Benchmark Capital, Index
Ventures
• Seasoned Executive Team
– Founded by Creator of Elasticsearch
– Seasoned Executives from SpringSource

7. Les enjeux de la recherche à
l’ère du BigData

8. Big Data in Todayʼs Business and Technology
Environment : some significant figures
• 2.7 Zetabytes of data exist in the digital universe today. (=1 billion Terabytes)
• 235 Terabytes of data has been collected by the U.S. Library of Congress in
April 2011.
• Facebook stores, accesses, and analyzes 30+ Petabytes of user generated
data.
• Akamai analyzes 75 million events per day to better target advertisements.
• Walmart handles more than 1 million customer transactions every hour,
which is imported into databases estimated to contain more than 2.5 petabytes
of data.
• The largest AT&T database boasts titles including the largest volume of data in
one unique database (312 terabytes) and the second largest number of rows in
a unique database (1.9 trillion), which comprises AT&Tʼ’s extensive calling
records.
• Hadoop :
– 94% of Hadoop users perform analytics on large volumes of data not
possible before
– 88% analyze data in greater detail;
– while 82% can now retain more of their data.

9. The Rapid Growth of Unstructured Data
• YouTube users upload 48 hours of new video every minute of the
day.
• 500+ new websites are created every minute of the day.
• Brands and organizations on Facebook receive 34,722 Likes every
minute of the day.
• 100 terabytes of data uploaded daily to Facebook.
• According to Twitterʼ’s own research in early 2012, it sees roughly
175 million tweets every day, and has more than 465 million
accounts.
• 30 Billion pieces of content shared on Facebook every month.
Data production will be 44 times greater in 2020 than it was in 2009.

10. Big Data & Real Business Issues
• 25+ % of decision‐makers surveyed predict that data volumes in their
companies will rise by more than 60% by the end of 2014, with the
average of all respondents anticipating a growth of no less than 42 %.
• 40% projected growth in global data generated per year vs. 5% growth in
global IT spending.
• According to estimates, the volume of business data worldwide, across all
companies, doubles every 1.2 years.
– Poor data can cost businesses 20%–35% of their operating revenue.
– Bad data or poor data quality costs US businesses $600 billion annually.
• 75+ % of decision-makers surveyed anticipate significant impacts in the
domain of storage systems as a result of the “Big Data” phenomenon.
• We anticipate a new challenge : to be able to Search and Analyse all
those datas … in real time !

11. Elasticsearch
A solution already in production
with significant french
implementations
Revolutionizing Data Search and
Analytics
Richard Maurer– SEMEA Territory Manager

12. Purpose of Elasticsearch
• Organize data and make it easily accessible
– Through powerful search and analytics
– Easily consumable (even for non-data scientists)
– Elegantly handles extremely large data volumes
– Delivers results in real time
• Technology stack agnostic
• Used across all market verticals

13. Features of Elasticsearch
• Structured & unstructured search
• Advanced analytics capabilities
• Unmatched performance
• Real-time results
• Highly scalable
• User friendly installation and maintenance

14. Elasticsearch 1.4: a solution
production ready
• Real time data Indexation
• Distributed
• High Availability
• Schema Free
• Real Time Data Analytics
• Multi Tenancy
• Much more….

15. Unprecedented Uptake
Elasticsearch has more than11 Million downloads
… and 650,000 more each month
Cumulative

16. French Users

17. French Use Cases
Bouygues Telecom:
Uses Elasticsearch in their Big Data Platform. Cut their web resolution time by 10X
Daily Motion:
Indexing their 20 million Videos on Elasticsearch. On production for over 2 years
Voyages SNCF
They have recently announced ES has being live on their “Usine Logicielle”
Fotolia:
Search Engine made on Elasticsearch, to access 24 Million Images, move over to ES
Orange:
With over 1.2 billion docs, looking at better solution and cost reduction

18. Product Offerings:
Support Throughout Your Project
1. Core Elasticsearch Training (2 days)
2. ELK Workshop (1 day)
3. Development and Production Support
4. Marvel, Monitoring of your ES clusters

19. 2: Support

20. Resources
• www.elasticsearch.com
• www.elasticsearch.org
• User Groups:
http://www.elasticsearch.org/community/forum/
• Contact:
Richard Maurer
Territory Manager
Richard.maurer@elasticsearch.com

21. MAKE SENSE OF YOUR (BIG) DATA!
David Pilato
Technical advocate!
!
elasticsearch.
@dadoonet

23. StartUp
data ?

24. StartUp

25. StartUp

26. StartUp

27. StartUp

28. StartUp

29. StartUp
BIG data ?

30. StartUp
BIG data ?

31. 35.000.000.000.000.000 mb
Source: http://www.csc.com/insights/flxwd/78931-big_data_just_beginning_to_explode
StartUp

32. StartUp
Source: http://www.domo.com/learn/data-never-sleeps-2

34. StartUp
search = like % ?
SELECT !
doc.*, country.* !
FROM !
doc, country!
WHERE !
doc.country_code = country.code AND!
doc.date_doc > to_date('2011-12', 'yyyy-mm') AND !
doc.date_doc < to_date('2012-01', 'yyyy-mm') AND !
lower(country.name) = 'france' AND !
lower(doc.comment) LIKE ‘%product%' AND
lower(doc.comment) LIKE ‘%david%';

35. StartUp
Search engine ?

36. elasticsearch ?
StartUp
plug & play
REST/JSON
scalable
Apache 2 license
Lucene
elasticsearch

37. Start…
$ wget https://download.elasticsearch.org/elasticsearch/
elasticsearch/elasticsearch-1.1.1.tar.gz!
$ tar -xf elasticsearch-1.1.1.tar.gz!
$ ./elasticsearch-1.1.1/bin/elasticsearch!
[INFO ][node ][Ghost Maker] {1.1.1}[5645]: initializing

38. … and play!
$ curl -XPUT localhost:9200/sessions/session/1 -d '{!
"title" : "Elasticsearch",!
"subtitle" : "Make sense of your (BIG) data !",!
"date" : "2014-05-20T10:30:00",!
"tags" : [ "elasticsearch", "alterway", "bigdata" ],!
"speakers" : [{!
"first_name" : "David", !
"last_name" : "Pilato" !
}]!
}'

39. Search!
$ curl http://localhost:9200/sessions/session/_search -d'
{
"query": {
"multi_match": {
"query": "elasticsearch alterway david",
"fields": [ "title^3", "tags^2", "speakers.first_name" ]
}
},
"post_filter": {
"range": {
"date": {
"from": "2014-05-01",
"to": "2014-06"
}
}
}
}'

40. StartUp
Compute?

41. Compute!
$ curl http://localhost:9200/sessions/session/_search -d'
{
"query": { ... },
"aggs": {
"by_date": {
"date_histogram": {
"field": "date",
"interval": "day",
"format" : "dd/MM/yyyy"
}
}
}
}'
"by_date": [
{ "key_as_string": "03/04/2014", "doc_count": 1 },
{ "key_as_string": "12/04/2014", "doc_count": 2 },
{ "key_as_string": "16/04/2014", "doc_count": 3 }
]

43. Let’s make sense of …
• logs!
• twitter!
• github!
• marketing data!
• ...!
• your data!
• your big data
#mstechdays #elasticsearch StartUp

44. Let’s make sense of …
• logs!
• twitter!
• github!
• marketing data!
• ...!
• your data!
• your big data
{
"name":"Pilato David",
"dateOfBirth":"1971-12-26",
"gender":"male",
"children":3,
"marketing":{
"fashion":334,
"music":3363,
"hifi":2351
},
"address":{
"country":"France",
"city":"Paris",
"location": [2.332395, 48.861871]
}
}
#mstechdays #elasticsearch StartUp

45. démo
MAKE SENSE OF YOUR (BIG) DATA!
let’s inject some marketing documents…
#mstechdays #elasticsearch StartUp

46. elasticsearch.
elasticsearch
kibana
logstash
Marvel

47. thanks
@dadoonet

48. Comment insérer ElasticSearch
dans votre Système d’Information
et en tirer le meilleur parti

49. ElasticSearch to do What ?

50. STORE

51. SEARCH

52. ANALYZE

53. Are you ready to use
ElasticSearch in your IT?

54. What you need to run it
• Java 8 update 20 or later, or Java 7 update 55 or
later
• Only Oracle’s Java and the OpenJDK are
supported.

55. Github projects
• Many projects
• Big activity
• Many languages
6 mois !

56. Clients

57. Scripting Plugins Language

58. Why it ‘s easy

59. • One to many
• ~ Zero conf
• Cloud oriented
• Scalability DNA
• Replication
• Sharding
• Distributed
• Resilience
• Snapshot
• Restore
Start Small Grow Big

60. • One to many
• ~ Zero conf
• Cloud oriented
• Scalability DNA
• Replication
• Sharding
• Distributed
• Resilience
• Snapshot
• Restore
Start Small Grow Big

61. Where / How can you use
ElasticSearch?

62. Centralized Log Storage 1/2
VIA

63. Centralized Log Storage 2/2

64. …
CMS Search Engine

65. Ecommerce Enhanced Search
Engine
• Faceting
• Fuzzy Search
• Speed
• Auto Completion
• Geo Search
• Log Analysis

66. Combining Hadoop & ElasticSearch
• REST based
• Memory and I/O efficient
• Adaptive I/O
• Map/Reduce API support
• Pig support
• Hive support
 elasticsearch-hadoop

67. What Else ?

68. It’s up to you to decide what to build with ES

69. Analysis / Dasboards
Some Examples

70. Kibana examples : IRC Activity

71. Kibana examples : Pfsense Monitoring

72. Kibana examples : Windows Events

73. Kibana examples : Inventory

74. Kibana examples : Syslog

75. Kibana examples : Web Activity

76. ES = No Limits

78. Conclusion

79. Conclusion
• Il est temps de révolutionner la façon dont vous valorisez
vos données : offrez Elasticsearch à vos applicatifs !
• La stack ELK (Elasticsearch, Logstash, Kibana) est déjà
massivement utilisée en production !
• Faites vous accompagner pour bénéficier des bonnes
pratiques et du support à tous les stades de votre projet :
conception, développement, production

80. Questions / Réponses