As digital consumption of rich media content explodes and with audience expectations at its peak, media providers have been challenged with not only delivering high-quality audience experiences but also the audience analytics in realtime to enable actionable insights for content publishers. Arkena, one of Europe’s leading media services organizations chose to power it’s analytical platform with Hortonworks Data Platform to cost effectively store and analyze over 3.5 terabytes of data per day. Join Hortonworks and Arkena as they share the industry challenges faced, the solution created which enables real-time and better analytics for their customers.

1. A Better Rich Media Experience & Video
Analytics at Arkena with Apache Hadoop
Welcome to today’s webinar.
We will begin shortly.
© Hortonworks Inc. 2011 – 2015. All Rights Reserved

2. Page2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Today’s Presenters
Reda Benzair
VP Technical Development, Arkena
John Kreisa
VP International Marketing, Hortonworks

3. A Better Rich Media
Experience & Video
Analytics at Arkena with
Apache Hadoop
Reda Benzair
VP Technical Development
Feb 2016

4. AGENDA
WHO WE ARE
CDN / OTT Business
1

5. AGENDA
WHO WE ARE
CDN / OTT Business
Why Media
Experience & Video
Analytics is so
Important for CDN.
21

6. AGENDA
WHO WE ARE
CDN / OTT Business
Why Media
Experience & Video
Analytics is so
Important for CDN.
Video Analytics
Challenge &
Difficulties
321

7. AGENDA
WHO WE ARE
CDN / OTT Business
Why Media
Experience & Video
Analytics is so
Important for CDN.
Video Analytics
Challenge &
Difficulties
Why we chosen
Hadoop Technology.
321
4

8. AGENDA
WHO WE ARE
CDN / OTT Business
Why Media
Experience & Video
Analytics is so
Important for CDN.
Video Analytics
Challenge &
Difficulties
Why we chosen
Hadoop Technology.
Architecture & Result
3
5
21
4

9. AGENDA
WHO WE ARE
CDN / OTT Business
Why Media
Experience & Video
Analytics is so
Important for CDN.
Video Analytics
Challenge &
Difficulties
Why we chosen
Hadoop Technology.
Architecture & Result Why we selected
Hortonworks
3
5
21
64

10. WHO WE ARE
YOUR TRUSTED MEDIA PARTNER
10
A TDF Group
Business Unit
• 16 POPs CDN
• 1 Tbps connectivity
• 400 live radios & 360 live TVs
• 630 hours of On Demand video processed daily
• United
Kingdom
• Norway
• USA
• Finland
• Denmark
• Poland
• France
• Spain
• Sweden
13 Offices in 9 Countries
A team of 400 employees
At a glance

11. CUSTOMERS
REFERENCES

12. MEDIA COMPANIES & OPERATORS
SOLUTIONS AND SERVICES
Cloud4Media
is our SaaS/PaaS
service that provides all
the necessary tools for
managing and
exchanging media
assets.
12
Cloud4Media
a SaaS/PaaS service
that provides all the necessary tools
for managing and exchanging
media assets.
PLAYOUT
optimized for the audiovisual
industry, designed to distribute
your live and
on-demand content.
OTT / CDN
solution with modular components
that enables content owners,
telecom operators and broadcasters
to provide video content to viewers
worldwide.
Video Platform
provides enterprises,
organizations and the public
sector with all-in-one tool to
publish, manage and distribute
video live or on-demand to every
device.
Play
is part of the Arkena Video
Platform and handles video
playback. Learn how to
customize PLAY to fit your
needs.
Mobile Publisher
is an add-on to the Arkena
Video Platform that lets you
publish live broadcasts and on
demand videos directly with
your Iphone.

13. CDN PLATFORM (CONTENT DELIVERY NETWORK)

14. ARKENA OTT / CDN
A UNIQUE EUROPEAN PRESENCE, ESPECIALLY FRANCE AND NORDICS
14
We offer advanced CDN solutions for media
 Live & OnDemand Streaming
 First-class Origin
 Transmuxing service
 Ads Insertion (Audio: Triton, Radionomy,
Adswizz)
 Timeshifting and Catch-up services
Arkena will offer a set of new
media analytics services
 Real Time Analytics
 Advanced Media Analytics
14

15. OTT SOLUTIONS (OVER-THE-TOP)

16. ARKENA OTT / CDN
A UNIQUE EUROPEAN PRESENCE, ESPECIALLY FRANCE AND NORDICS
1616
Content management & animation
• Metadata and catalog organization
• Offer scheduling and promotions
• Subscription, rental and purchase models
• Automatic sorts and optimized API for OTT apps display
User accounts management
• User ownership tracking
• DRM entitlements
• Device pairing and restrictions
• Multiscreen favorites and resume
Content processing & protection
• Adaptive streaming and download support
• Multiple audio tracks and subtitles support
• Smooth streaming with Playready and DASH with Marlin
• Geoblocking and streaming limits

17. 17
Arkena OTT / CDN Analytics
Challenge
“Infrastructure capable of handling Millions
of simultaneous connections/requests”.

18. CDN Architecture
 Media specialized CDN with strong presence in Europe,
especially France and Nordics: audiovisual media streaming-
dedicated CDN
 Video and Audio delivery, Live and On-Demand services
 Multiscreen workflow expertise and broadcast / IP convergence
18
We deliver your contents with optimal performance
on all devices
NETWORK
IP
Regional
Network
CACHING SERVER
ORIGIN /
TRANSMUX SERVICE
PoP
CACHING SERVER
PoP
CACHING SERVER
PoP
IP
Regional
Network
IP
Regional
Network
 More than 300 CDN customers in Europe.
 With 16 European PoPs, local to final end-users
 Capacity: 1Tbps, very high storage capacity (~PB)
 More than 1000 streaming servers.

19. CDN Architecture
 Media specialized CDN with strong presence in Europe,
especially France and Nordics: audiovisual media streaming-
dedicated CDN
 Video and Audio delivery, Live and On-Demand services
 Multiscreen workflow expertise and broadcast / IP convergence
19
We deliver your contents with optimal performance
on all devices
NETWORK
IP
Regional
Network
CACHING SERVER
ORIGIN /
TRANSMUX SERVICE
PoP
CACHING SERVER
PoP
CACHING SERVER
PoP
IP
Regional
Network
IP
Regional
Network
 More than 300 CDN customers in Europe.
 With 16 European PoPs, local to final end-users
 Capacity: 1Tbps, very high storage capacity (~PB)
 More than 1000 streaming servers.
CLUSTER
Logs trafic

20. Why Media Experience & Video Analytics is so Important
20
Customer Trust
Real Time AnalyticsAdvanced Metrics
Advanced Media Analytics
to monetize your audience.
Billing & Payment
Reporting, Billing
20
Why we need an Efficient
Analytics System

21. 21
Data overload every second

22. daily raw log size
(uncompressed, no replication)
20 GB
to 200 GB per day
Video Analytics Challenge
a peak rate of 60K
Events/Second
keep raw logs for 3-9 months
average raw log data
input rate
20 Mbps
to 120 Mbps

23. daily raw log size
(uncompressed, no replication)
20 GB
to 200 GB per day
Video Analytics Challenge
We compute15 Metricsat every batch:
Volume, Hits, Session duration, Concurrent sessions, Unique viewers...
All metrics are available over 15 Dimensions
Country, City, User agent, Browser, HTTP status code...
Real time statistics should be provided in
3 min
a peak rate of 60K
Events/Second
keep raw logs for 3-9 months
average raw log data
input rate
20 Mbps
to 120 Mbps

24. daily raw log size
(uncompressed, no replication)
20 GB
to 200 GB per day
Video Analytics Challenge
We compute15 Metricsat every batch:
Volume, Hits, Session duration, Concurrent sessions, Unique viewers...
All metrics are available over 15 Dimensions
Country, City, User agent, Browser, HTTP status code...
Real time statistics should be provided in
3 min
a peak rate of 60K
Events/Second
keep raw logs for 3-9 months
1 CDN "Edge" server generates
an average of
15 – 22 Million
Lines/Day
DASHAdaptative Bitrate
Streaming
average raw log data
input rate
20 Mbps
to 120 Mbps
1 Movie (HD, 1 hour) in
DASH format with
8 Video Tracks
1 Audio Track
4200 log
events

25. 25
Video Analytics Challenge
Difficulty & Challenge

26. 26
Video Analytics Challenge
Safely Transport the data in
Real time from differents POP
to the DATA cluster.
TRANSPORT

27. 27
Video Analytics Challenge
Make life easy for the Operation
OPERATION

28. 28
Video Analytics Challenge
Store the data safely over a
long period.
Compute the Metrics in Real
Time.
Consolidate in Batch
STORE DATA

29. 29
Video Analytics Challenge
Compute the Analytics metrics
in Real Time.
Compute DATA in
Real Time

30. 30
Video Analytics Story
2012 20142013 2015
Arkena Analytics has
built and developed
In House.
There is a major problem
in production with a
significant downtime.
Home made Open Source

31. 31
Video Analytics Story
2012 20142013 2015
Arkena Analytics has
built and developed
In House.
There is a major problem
in production with a
significant downtime.
Analysis of the market.
Make or Buy
Launching of the project
with the partners. Build
the team (1 Project
Manager, 1 Developer , 1
System Engineer)
Home made Open Source
POC

32. 32
Video Analytics Story
2012 20142013 2015
V 1
Arkena Analytics has
built and developed
In House.
There is a major problem
in production with a
significant downtime.
Analysis of the market.
Make or Buy
Launching of the project
with the partners. Build
the team (1 Project
Manager, 1 Developer , 1
System Engineer)
Release the Analytics
Platform to the operation
team and open the
services to the customers.
Home made Open Source
POC

33. 33
Video Analytics Challenge
Hadoop Technology

34. 34
TRANSPORT
Flume
 Apache Flume is a distributed, reliable, and available service
for efficiently collecting, aggregating, and moving large
amounts of streaming data into the HDP cluster.
 Flume already Integrated in HDP: YARN coordinates data
ingest from Apache Flume and other services that deliver raw
data into an HDP cluster.
Rsyslog
 RSYSLOG is the rocket-fast system for log processing. It offers
high-performance, great security features and a modular
design to transport data from our Edge.
 We use the RELP protocol (The Reliable Event Logging
Protocol). protocol to provide reliable delivery of event
messages.
Transport Safe

35. 35
STORE DATA
Shared data set
 In-house solution: can't query the whole data set
 HDP: single entry point from HDFS, can query and
cross-correlate everything from the beginning of times
(almost).
Opportunities
 In-house solution: Rigid, A nightmare for the
operational teams.
 HDP: Give us new opportunities (Machine learning, new
metrics,…).
Stability & Trust
Hortonworks Data Platform
 In-house solution: add clusters to scale out (we had
3!)
 HDP: add nodes to scale out (storage + compute)

36. 36
OPERATION
Reliability &
Scalability
YARN
 View your cluster as a single Data Operating System
 Run multiple jobs on multiple processing engines
 High availability with Standby Resource Manager
 Easy scale-out by adding more YARN NodeManagers
Queue Management
 Make sure business-critical jobs never lack resources
 Separate operation tasks from business tasks
 Validate new jobs' versions with no production impact

37. 37
OPERATION
Compute Real Time HDP Stack SPARK Streaming
 HDP packages and incorporates the most recent and hadoop
software technology in the same Stack (Spark, Hive,Tez,…).
 Apache Spark is a fast, in-memory data processing engine.
Process the data very 2 min.
 HDP YARN-based architecture provides the foundation that
enables Spark and other applications to share a common
cluster and dataset while ensuring consistent levels of service
and response.

38. 38
OPERATION
Compute Real Time HDP Stack SPARK Streaming
 HDP packages and incorporates the most recent and hadoop
software technology in the same Stack (Spark, Hive,Tez,…).
 Apache Spark is a fast, in-memory data processing engine.
Process the data very 2 min.
 HDP YARN-based architecture provides the foundation that
enables Spark and other applications to share a common
cluster and dataset while ensuring consistent levels of service
and response.
 Use Architecture Lambda
• Processing real Time : Spark Streaming.
• Synchronize the Data in the HDFS.
• Consolidate the data with Hive/Tez.
• Ingest in the ElasticSearch.
Events
Near Real
Time
Store Batch

39. 39
OPERATION
Reduce operational
cost day-to-day
Easy To Use for the long run
 Easy Setup and Installation.
 Machine provisioning and capacity planning.
 Easier Provisioning and Faster Cluster Deployment
Ambari
 Expand clusters automatically as new nodes come
online
 Track cluster health, job progress and KPIs with alerts,
customizable views, customizable dashboards...
 REST API making deployment & configuration easy to
automate with modern conf management tools (Ansible)

40. ARKENA CDN : Analytics Cluster
Transport

41. ARKENA CDN : Analytics Cluster
HDP ComputeTransport

42. ARKENA CDN : Analytics Cluster
IndexingHDP Compute
Customer
Front-End
Transport

43. 43
ARKENA CDN : HDP Cluster
1 2
3 4
5
Live ProcessingBatch Processing
Transport Multiple Processing Archivage Operations

44. 44
ARKENA CDN : Hardware Cluster
A peak rate of
60K Events/Second
keep raw logs for
3-9 months
HDP Compute Cluster :
 We made the choice on DELL R730 the configuration we have
set with 16 Core, 128G RAM and 14 disk with 1To.
 We attempted to respect the rule of thumb for Hadoop of
 (1 Disk -> 8G RAM -> 1 physical core) in order to optimize the
I/O performances with 10 file channel per machine and we
kept 2 disk for the system.
ElasticSearch Cluster :
 We choice 5 machines M610 in order to have an odd number
for the redundancy and the failover

45. 45
ARKENA CDN : Hardware Cluster
A peak rate of
60K Events/Second
keep raw logs for
3-9 months
Elastic Search Cluster
5 Machines
Cluster API
6 VM
HDP Cluster
8 Machines
HDP Compute Cluster :
 We made the choice on DELL R730 the configuration we have
set with 16 Core, 128G RAM and 14 disk with 1To.
 We attempted to respect the rule of thumb for Hadoop of
 (1 Disk -> 8G RAM -> 1 physical core) in order to optimize the
I/O performances with 10 file channel per machine and we
kept 2 disk for the system.
ElasticSearch Cluster :
 We choice 5 machines M610 in order to have an odd number
for the redundancy and the failover

46. 46
ARKENA CDN : Transport
Form Edge to Cluster
 Rsyslog transport the logs from Edge to log
aggregator component.
 Feature available on Rsyslog :
 RELP protocol
 Native in Linux
 Disk Assit Queue beffuering
Ingest to HDFS
 Apache Flume is used to fetch the logs from
Rsyslog and push them to HDFS.
Transport Technology
 It’s not just how quickly you move data, but how
move safly from the Edge to the Cluster without
losing any lines.
 How we can have resilient solution : mixed 2
softwares.

47. 47
ARKENA CDN : Transport
The log aggregator (with Rsyslog)
 The log aggregator is responsible for reliably
forwarding the logs to the Compute Cluster.
 If the compute cluster is unavailable or networks
issue, the logs are spooled on disk, and stay on the
aggregators until the compute cluster comes back
online.
 Logs are sent from the edge servers to Log
Aggregators. There is one aggregator per PoP.
 log aggregators are not specific to any PoP, we could
reproduce this setup on any PoP, hereby designated
as "PoPx" or "PoPy", just by deploying generic log
aggregators.

48. 48
ARKENA CDN : Transport
The log aggregator (with Rsyslog)
 The log aggregator is responsible for reliably forwarding the
logs to the Compute Cluster.
 If the compute cluster is unavailable or networks issue, the
logs are spooled on disk, and stay on the aggregators until
the compute cluster comes back online.
 Logs are sent from the edge servers to Log Aggregators.
There is one aggregator per PoP.
 log aggregators are not specific to any PoP, we could
reproduce this setup on any PoP, hereby designated as
"PoPx" or "PoPy", just by deploying generic log aggregators.

49. 49
ARKENA CDN : HDFS
Ingest into HDFS
 The logs are ingested in HDFS once the local
Rsyslog on each Hadoop node receives an event.
 Apache Flume is used to fetch the logs from
Rsyslog and push them to HDFS.
 The local Rsyslog forwards an event to the local
Flume agent (TCP connection to `localhost`)
 The Flume agent then proceeds to send the logs to
HDFS, while buffering them on disk for durability
reasons.

50. 50
ARKENA CDN : HDFS
Ingest into HDFS
 The logs are ingested in HDFS once the local
Rsyslog on each Hadoop node receives an event.
 Apache Flume is used to fetch the logs from
Rsyslog and push them to HDFS.
 The local Rsyslog forwards an event to the local
Flume agent (TCP connection to `localhost`)
 The Flume agent then proceeds to send the logs to
HDFS, while buffering them on disk for durability
reasons.

51. 51
ARKENA CDN : HDFS
Ingest into HDFS
 An SyslogSource, listening on a TCP socket, receives
the incoming rsyslog event
 a "FileChannel" listens for incoming events on the
rsyslog TCP source, and writes them locally to 10
different "datadirs" on 10 separate physical hard disk
drives.
 Each datadir acts as a FIFO. Load is balanced evenly
from the single Rsyslog TCP source to the 10 datadirs
 The "FileChannel" is plugged to 4 "HDFS Sinks".
When enough events have been buffered in the
channel, those events are sent to the 4 HDFS sinks in
an evenly balanced fashion.

52. 52
ARKENA CDN : Customer Front-End

53. 53
ARKENA CDN : Spark Streaming
Events
Near Real
Time
Store
Events
1
1
1

54. 54
ARKENA CDN : Spark Streaming
Events
Near Real
Time
Store Batch
Events
2
3

55. Arkena choose Hadoop
(Hortonworks)

56. 56
Why we selected Hortonworks
Avoid Vendor Lock In
 Hortonworks Data Platform is close to the open
source trunk as possible and is developed 100%
in the open so you are never locked in.
 Present a single, tested and completely open
Hadoop platform with no proprietary bolt-ons.
Transparency
 Price Model & Unlimited Support Throughout
our projects
“Hortonworks loves and lives
open source innovation”,
Arkena does as well!

57. 57
Why we selected Hortonworks
Connect With the Community
 We employ a large number of Apache project
committers & innovators so that you are
represented in the open source community.
 Only Hortonworks can deliver the deepest level
of support across all the components of the
Hadoop platform.
Support from the Experts
 They provide the highest quality of support for
deploying at scale.
“Hortonworks loves and lives
open source innovation”,
Arkena as well.

58. 58

59. 59
What Happened after the Release
We have identified some improvement items after
the production release.

60. 60
What Happened after the Release
We have identified some improvement items after
the production release.
Transport

61. 61
What Happened after the Release
We have identified some improvement items after
the production release.
Transport Operation

62. 62
About The team
Reda Benzair
Projet Roles :
Architect & Project management
Work Experience
Executive MBA, Graduate from Engineering
School and Master of Advanced Study
university (DEA). 15 years of experience in
SmartJog SAS (become Arkena in 2013)
TDF subsidiary. Since 2013 VP Technical
development, leading technical
development team located in Paris,
Stockholm and Warsaw.
Projet Roles : Senior Software Engineer,
Spark, System
Work Experience
A passionate programmer with a strong
interest in devops and software
craftmanship, Erwan has been working on
complex distributed architectures during
the last 10 years. He joined Arkena as a
general-purpose Analytics engineer,
worked on the Hadoop data processing
pipeline, developped a decent chunk
Erwan Queffelec Julien Girardin
Projet Roles : Senior System
administrator and python developper.
Work Experience
A passionate with Linux system with a
strong interest in devops and python
development. Strong experience with
complex distributed architectures.

63. Page63 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
ONLY
100open source
Apache Hadoop data platform
% Founded in 2011
HADOOP
1ST
provider to go public
IPO 4Q14 (NASDAQ: HDP)
subscription
customers
800+ employees across
~850
countri
es
technology partners
1,600+ 16TM
About Hortonworks
Fastest enterprise software company to reach $100 million in
annual revenue
(Barclays research, 2015)

64. Page64 © Hortonworks Inc. 2011 – 2016. All Rights ReservedPage64
Social
Mapping
Payment
Tracking
Factory
Yields
Defect
Detection
Call
Analysis
Machine
Data
Product
Design
M & A
Due
Diligence
Next
Product
Recs
Cyber
Security
Risk
Modeling
Ad
Placement
Proactive
Repair
Disaster
Mitigation
Investment
Planning
Inventory
Predictions
Customer
Support
Sentiment
Analysis
Supply
Chain
Ad
Placement
Basket
Analysis
Segments
Cross-
Sell
Customer
Retention
Vendor
Scorecards
Optimize
Inventories
OPEX
Reduction
Mainframe
Offloads
Historical
Records
Data
as a
Service
Public
Data
Capture
Fraud
Prevention
Device
Data
Ingest
Rapid
Reporting
Digital
Protection

65. Hadoop Summit 2016 - Dublin
Date: Wednesday 13 – Thursday 14 April, 2016
Venue: Convention Centre Dublin
Website: www.hadoopsummit.org
Why Should You Attend?
• Hadoop Summit is Europe’s premier industry event for Apache Hadoop users, developers and vendors
• Two full days of practical and cutting edge education designed by the community – for the community
• Over 90 sessions spanning 7 tracks dedicated to enabling the next generation data platform
• A Community Showcase featuring the industries who’s who
• Crash courses for those just beginning with Hadoop
• Community driven meetups
• Birds of a Feather (BoFs) meetings to promote collaboration
• Comprehensive pre-event hands on classroom training
• A social program which provides ample opportunity to network and make new industry connections
• An amazing event party at the Guinness Storehouse Brewery
Plus much much more!
Register Now to take advantage of our Early Bird rates!

66. Page66 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Questions & Next Steps
Attend our next webinar
Download the sandbox
Try Hortonworks Data Flow
Page66 © Hortonworks Inc. 2011 – 2015. All Rights Reserved

67. 67
Why we selected Hortonworks
Thank you for your attention!