ignacio mulas viela_massively distributed analytics_dataconomy_stockholm
TRANSCRIPT
Ignacio Mulas Viela
Ericsson Research
Massively distributed analytics
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 2
About myself…
Name: Ignacio Mulas Viela
Contact channels:
@ immulvi
Experience @ Ericsson
Current: Researcher and project leader in
Cloud Analytics in the Machine Learning
group
Previous: Big Data, Cloud and Automation
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 3
› Industry evolution
› Data economics
› Architectures
› Conclusions
› Q&A
agenda
Vision 2020
Networked society50 billion connected devices
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 5
PACE OF CHANGE
1 billion connected places
50 billion connected things
PLACES
PEOPLE
THINGS
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 6
› Technological challenges
– Data sources are geographically distributed
› … And highly cross-referenced
– No unique identifier
– Timing and order carries meaning
– Demanding delay constraint:
› For radio optimization: ms
› Core network optimization: ms to s
› Software services: s to min
› Interpretation challenges
– Low-level data -> hard to associate with business-level
meanings
– IoT/M2M data is
› Structured in the small picture: protocol level
› Unstructured in the big picture: Heterogeneous
environment use different formats/protocols
challenges
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 7
“IoT ties to big data as the massive amount of rapidly moving and
freely available data that potentially serves a valuable and unique
need in the marketplace, but is extremely expensive and
difficult to mine by traditional means”
By Techrepublic
Source: http://www.techrepublic.com/blog/big-data-analytics/big-data-defined/
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 8
Source (year 2011): http://radar.oreilly.com/2011/08/building-data-startups.html
James Hamilton network costs in Amazon (minute 4 to 15): https://www.youtube.com/watch?x-yt-ts=1422579428&v=JIQETrFC_SQ&feature=player_embedded&x-yt-cl=85114404
Data economics
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 9
Wait a moment…
Huge data volumes, time constraints and distributed data…
Are you crazy?
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 10
Centralized architecture
…
RAW DATA
PROCESSED DATA
TOOLS
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 11
De-Centralized architecture
…
RAW DATA
PROCESSED DATA
TOOLS
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 12
DISTRIBUTED architecture
…
RAW & PROC. DATA
PROCESSED DATA
TOOLS
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 13
› Centralized:
– “Move everything there, I have capacity to analyze everything!”
– Stream and compute
› Decentralized:
– “You can do this task but when you know about that, let me handle it!”
– Stream, compute and stream
› Distributed:
– “We live in harmony with no bosses”
– Compute and stream
Analytics principles
High
Low
BA
ND
WID
TH
CP
U D
EN
SIT
Y
Big Data, Stockholm | Ericsson Internal | 2015-04-01 | Page 14
› Moving the data around is expensive
› Define concrete requirements
– “I am doing analytics” is not enough…
› One approach don’t rule them all
– Go with the simplest solution that matches your needs
conclusions
Also, feel free to contact me if you have suggestions/comments/questions @ [email protected]
Thank you!quESTIONS?
