The document presents an overview of big data challenges and the need for effective management and analysis strategies. It emphasizes the importance of integrating algorithms, machines, and human intelligence to enhance data processing and extract meaningful insights. Additionally, it highlights the growing volume of data generated by various sectors and the significant costs associated with storage and management.

1. 5/10/2012
A Berkeley View of Big Data
Anthony D. Joseph
UC Berkeley
EDUSERV Symposium
10 May 2012
Who Am I?
• Research:
– Internet-scale systems (RAD Lab, AMP Lab)
– Security (DETERlab Testbed)
– Adversarial machine learning (SecML)
• Teaching (undergrad/grad): operating
systems and systems, security, networking
Disclaimer: I don’t speak for UC or our
research sponsors
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2. 5/10/2012
Big Data is Massive…
• Facebook:
– 130TB/day: user logs
– 200-400TB/day: 83 million pictures
– >40 Billion photos
• Google: > 25 PB/day processed data
• Data generated by LHC: 1 PB/sec
• Total data created in 2010: 1.ZettaByte
(1,000,000 PB)/year
– ~60% increase every year
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…and Diverse…
• Walmart
– >1 million customer
transactions/hr
– >2.5 PByte customer DB
• Human genome sequencing
– Analyzing 3 billion base pairs
– Ten years for first one (2003)
– Today, less than one week
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3. 5/10/2012
…and Novel…
• Analyzing data from user behavior vs user input
• USGS TED
– Twitter-based Earthquake Detector
• Google Trends: “nowcasting”
– http://www.google.org/flutrends/
– US 2009 “Cash for Clunkers” program success
– US State unemployment rates
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…and Grows Bigger…
• More and more devices
• More and more people
• Cheaper and cheaper storage
– ~50% increase in GB/$ every year
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4. 5/10/2012
…and Bigger!
• Log everything!
– Don’t always know what question you’ll need
to answer
• Stored data
growing faster
than both
available
storage and GB/$
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Which Big Data to Keep?
• Hard to decide what to delete
– Thankless decision: people know only when you
are wrong!
– “Climate Research Unit (CRU) scientists admit
they threw away key data used in global warming
calculations”
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5. 5/10/2012
Data Retention Requirements
• New NSF data retention requirements
– Proposals submitted after 18 January 2011
must include a “Data Management Plan”
– Have to keep all data (including metadata) for
3 years after research award conclusion
– Institutional/org considerations:
• Opportunity to invest in pooled storage: campus,
systemwide, regional, …
• Typical cost: 8TB chunks at $1.44/GB/year
collaborative space and $0.17/GB/year for archive
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space
Big Data Isn’t Always Big
Data that is expensive to manage,
and hard to extract value from
• You don’t need to be big to have big data problem!
– Inadequate tools to analyze data
– Data management may dominate infrastructure cost
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6. 5/10/2012
Big Data is not Cheap!
• Storing and managing 1PB
data: $500K-$1M/ year
– Facebook: 200 PB/year
100%
• “Typical” cloud-based
Infrastructure cost
80%
service startup (e.g., 60% ~1PB storage capacity
Conviva) 40%
– Log storage dominates 20%
infrastructure cost 0%
2007 2008 2009 2010
Storage cluster Other
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Hard to Extract Value from Data!
• Data is
– Diverse, variety of sources
– Uncurated, no schema, inconsistent semantics, syntax
– Integration a huge challenge
• No easy way to get answers that are
– High-quality
– Timely
• Challenge: maximize value from data by getting
best possible answers
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7. 5/10/2012
Requires Multifaceted Approach
• Three dimensions to improve data
analysis
– Improving scale, efficiency, and quality of
algorithms (Algorithms)
– Scaling up datacenters (Machines)
– Leverage human activity and intelligence
(People)
• Need to adaptively and flexibly combine all
three dimensions
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The State of the Art
• Today’s apps: fixed point in solution space
Algorithms
Watson/IBM
search
Machines
People
Need techniques to dynamically pick best
14
operating point
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8. 5/10/2012
What Is the Big Data Problem?
• For two main reasons:
– the more data the greater chance to find any
pattern you’d like to find
• the more rows in a table, the more columns
• the more columns, the more hypotheses that can
be considered
• indeed, the number of hypotheses grows
exponentially in the number of columns
– the more data the less likely a sophisticated
ML algorithm will run in an acceptable time
frame
• and then we have to back off to cheaper
algorithms that may be more error-prone
A Formulation of the Problem
• Given an inferential goal and a fixed
computational budget, provide a guarantee
(supported by an algorithm and an analysis) that
the quality of inference will increase
monotonically as data accrue (without bound)
– This is far from being achieved in the current state of
the literature!
• It can be achieved by building a scalable system
that blends statistical and computational design
principles
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9. 5/10/2012
Big Data in the US
• Many Fortune 1000+ companies with huge write
once, read none big data collections
– For all the reasons I’ve already outlined…
• US Government agencies in same situation
– New R&D funding
• Many companies developing proprietary solutions
• Very active open source big data tools committee
– Broad international participation
– Data Without Borders helping non-profits through pro
bono data collection, analysis, and visualization
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Significant USG Investment
• 29 March 2012
– US federal agencies announced more than
$200 million in new commitments
– Dept of Defense, Dept of Homeland Security,
Dept of Energy, Veterans Administration, Office
of Scientific and Technical Information, Health
and Human Services, Food and Drug Admin,
National Archives & Records Admin, National
Aerospace & Space Admin, National Institutes
of Health, National Science Foundation,
National Security Agency, US Geological
18 Service
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10. 5/10/2012
Active Open Source Community
• On-going development of several elements
of Big Data analysis pipeline
• Apache Hadoop (MapReduce)
• Hive
• Apache Pig
• R / Octave
• Much more is needed!
• E.g., new analysis environments
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The AMP Lab
Make sense of data at scale by tightly
integrating algorithms, machines, and people
Algorithms
Watson/IBM
search
Machines
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People
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11. 5/10/2012
AMP Faculty and Sponsors
• Faculty
– Alex Bayen (mobile sensing platforms)
– Armando Fox (systems)
– Michael Franklin (databases): Director
– Michael Jordan (machine learning): Co-director
– Anthony Joseph (security & privacy)
– Randy Katz (systems)
– David Patterson (systems)
– Ion Stoica (systems): Co-director
– Scott Shenker (networking)
• Sponsors:
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Algorithms
• State-of-art Machine Learning (ML)
algorithms do not scale
– Prohibitive to process all data points
Estimate
true answer
How do you know
when to stop?
# of data points
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12. 5/10/2012
Algorithms
• Given any problem, data and a budget
– Immediate results with continuous improvement
– Calibrate answer: provide error bars
Estimate
true answer
Error bars on every
answer!
# of data points
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Algorithms
• Given any problem, data and a time budget
– Immediate results with continuous improvement
– Calibrate answer: provide error bars
Estimate
true answer
Stop when error
smaller than a given
threshold
# of data points
24 time
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13. 5/10/2012
Algorithms
• Given any problem, data and a time budget
– Automatically pick the best algorithm
Estimate
simple
true answer
sophisticated
error pick
too high sophisticated pick simple
time
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Machines
• “The datacenter as a computer” still in its
infancy
– Special purpose clusters, e.g., Hadoop cluster
– Highly variable performance
– Hard to program
– Hard to debug
=?
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14. 5/10/2012
Machines
• Make datacenter a real computer!
• Share datacenter between multiple cluster computing
apps
• Provide new abstractions and services
AMP
stack
Datacenter “OS” (e.g., Mesos)
Existing
Node OS Node OS … Node OS
(e.g. Linux) (e.g. Windows) (e.g. Linux) stack
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Machines
• Make datacenter a real computer!
Support existing
Hive
Cassandra
Hypertbale
cluster computing
MPI
Hadoop
…
apps
AMP
stack
Datacenter “OS” (e.g., Mesos)
Node OS Node OS Node OS
Existing
… stack
(e.g. Linux) (e.g. Windows) (e.g. Linux)
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15. 5/10/2012
Machines
• Make datacenter a real computer!
Predictive &
Support interactive insightful query
and iterative data language
analysis (e.g., ML
Hive
Cassandra
Hypertbale
PIQL
Spark
MPI
algorithms)…
Hadoop
…
SCADS AMP
stack
Consistency
Datacenter “OS” (e.g., Mesos)
adjustable data
Node OS Node OS store Node OS
Existing
… stack
(e.g. Linux) (e.g. Windows) (e.g. Linux)
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Machines
• Make datacenter a real computer!
Applications, tools
Hive
Cassandra
Hypertbale
PIQL
Spark
• Advanced ML algorithms
MPI
Hadoop
… …
• Interactive data mining
SCADS
AMP
• Collaborative visualization stack
Datacenter “OS” (e.g., Mesos)
Node OS Node OS Node OS
Existing
… stack
(e.g. Linux) (e.g. Windows) (e.g. Linux)
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16. 5/10/2012
People
• Humans can make sense of messy data!
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People
• Make people an integrated part of
the system!
– Leverage human activity
Machines +
– Leverage human intelligence
(crowdsourcing): Algorithms
• Curate and clean dirty data
Questions
activity
Answers
• Answer imprecise questions
data,
• Test and improve algorithms
• Challenge
– Inconsistent answer quality in all
dimensions (e.g., type of question,
time, cost)
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17. 5/10/2012
Real Applications
• Mobile Millennium Project
– Alex Bayen, Civil and Environment
Engineering, UC Berkeley
• Microsimulation of urban
development
– Paul Waddell, College of
Environment Design, UC Berkeley
• Crowd based opinion formation
– Ken Goldberg, Industrial
Engineering and Operations
Research, UC Berkeley
• Personalized Sequencing
– Taylor Sittler, UCSF
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Personalized Sequencing
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18. 5/10/2012
The AMP Lab
Make sense of data at scale by tightly
integrating algorithms, machines, and people
Algorithms
Microsimulation
Mobile
Millennium
Sequencing
Machines
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People
Big Data in 2020
Are you prepared?
• To create a new generation of big data scientist
• For ML to become an engineering discipline
• For people to be deeply integrated in big data
analysis pipeline
• Will your institution
– offer a big data curriculum touching all fields?
– have hired cross-disciplinary faculty?
– have invested in (pooled) storage infrastructure?
– have invested in public/private clouds?
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– have built inter/intra campus networks?
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19. 5/10/2012
Summary
• Goal: Tame Big Data Problem
– Get results with right quality at the right time
• Approach: Holistic integration of
Algorithms, Machines, and People
• Huge research issues across many
domains
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