AI&BigDataDay 2017 Andrey Okhrimets - “Data Lake and Media Asset Management. Challenges and outcomes” Сайт: www.aiconf.com.ua FB group: www.fb.com/groups/974150252615820/

1. Data lake for media
asset management
Andriy Okhrimets
VP Development, Svitla

2. Andriy Okhrimets
• 10+ years in media industry
• 13 years in Project
management
• Professional Photographer
• Author of couple pro
imaging tools

3. Agenda
• Brief MAM industry overview.
• Recent changes and data consolidation approach.
• Media ingest in Big Data world.
• Data Lake in media organization.
• Metadata and unstructured media.
• Content adaptation and content consumption.
• Business intelligence and metadata
• Summary and recommendations

4. What is MAM?

5. Storage to metadata connection

6. More complex example
Movie production

7. More complex example
Security systems

8. Problem definition:
• Unstructured data - Really?
• Metadata indexing and creation
• Search

9. Media ingest:
• Moved from analog to digital
• Still is structured process
• Semi automated
on small scale.
• More
automation on
large scale

10. Media ingest problems:
• Need automated metadata
creation

11. Media ingest problems:
• Need automated metadata
creation

12. Each MAM has each own structure tailored to specific usage

13. Each MAM has each own structure tailored to specific usage

14. Can you merge two MAM?

15. Data lake?

16. Data lake as a service for MAM
Media Ingest MIO
MIO
MIO
MAM
index
Media Ingest
Worker

17. Data lake as storage
Media Ingest
MAM
1
Media Ingest
Worker n
MAM
2
MAM
n

18. Clean and dirty data lake
Media import
Media Ingest
Worker n
BLOB 1
BLOB 2
BLOB n
Worker 2
Worker 1
MIO
MIO
MIO

19. Clean and dirty data lake
Viewers data
Session logs
BLOB 1
Logs
Statistics
MetaW1
MIO
MIO
MIO

20. Dirty lake need to be cleaned

21. Key takeaways
• Third party workers can be plugged in with ease
• Scalability of system now divided into storage and
workers
• Data scientist can find new way of creating
metadata
• Vendors need to agreed on shared formats
• Still need orchestration system
• Access to a system can be scaled and no longer
proprietary

22. Example
<video height="270" width="480" controls
poster="http://d3mk7n3cl27pjb.cloudfront.net/bigbuckbunny/bunny-00004.png"
src src="http://d1s3yn3kxq96sy.cloudfront.net/bigbuckbunny/index.m3u8">
<p>Your browser does not support HLS playback in HTML5.</p>
</video>
• Amazon cloud streaming
• Amazon transcoding services
• Amazon CDN

23. Example

24. Example

25. Example

26. Example

27. Thank you
a.okhrimets@svitla.com