Takeaway: Thoran Rodrigues
interviewed Dr. Satwant Kaur about the 10 emerging technologies that will drive
Big Data forward.
I’ve recently had the opportunity to
have a conversation with Dr. Satwant Kaur on the topic of Big Data (see my
previous interview with Dr. Kaur, “The 10
traits of the smart cloud“). Dr. Kaur has an extensive
history in IT, being the author of Intel’s Transitioning Embedded Systems to
Intelligent Environments. Her professional background, which includes four
patents while at Intel & CA, 20 distinguished awards, ten keynote
conference speeches at IEEE, and over 50 papers and publications, has earned
her the nickname, “The First Lady of Emerging Technologies.” Dr. Kaur will be
delivering the keynote at the CES show: 2013 IEEE International Conference on
Consumer Electronics (ICCE).
While the topic of Big Data is broad
and encompasses many trends and new technology developments, she managed to
give me a very good overview of what she considers to be the top ten emerging
technologies that are helping users cope with and handle Big Data in a
cost-effective manner.
Dr.
Kaur:
Column-oriented databases
Traditional, row-oriented databases
are excellent for online transaction processing with high update speeds, but
they fall short on query performance as the data volumes grow and as data
becomes more unstructured. Column-oriented databases store data with a focus on
columns, instead of rows, allowing for huge data compression and very fast
query times. The downside to these databases is that they will generally only
allow batch updates, having a much slower update time than traditional models.
Schema-less databases, or NoSQL
databases
There are several database types
that fit into this category, such as key-value stores and document stores,
which focus on the storage and retrieval of large volumes of unstructured,
semi-structured, or even structured data. They achieve performance gains by
doing away with some (or all) of the restrictions traditionally associated with
conventional databases, such as read-write consistency, in exchange for
scalability and distributed processing.
MapReduce
This is a programming paradigm that
allows for massive job execution scalability against thousands of servers or
clusters of servers. Any MapReduce implementation consists of two tasks:
·The
“Map” task, where an input dataset is converted into a different set of
key/value pairs, or tuples;
·The
“Reduce” task, where several of the outputs of the “Map” task are combined to
form a reduced set of tuples (hence the name).
Hadoop
Hadoop is by far the most popular
implementation of MapReduce, being an entirely open source platform for
handling Big Data. It is flexible enough to be able to work with multiple data
sources, either aggregating multiple sources of data in order to do large scale
processing, or even reading data from a database in order to run
processor-intensive machine learning jobs. It has several different
applications, but one of the top use cases is for large volumes of constantly
changing data, such as location-based data from weather or traffic sensors,
web-based or social media data, or machine-to-machine transactional data.
Hive
Hive is a “SQL-like” bridge that
allows conventional BI applications to run queries against a Hadoop cluster. It
was developed originally by Facebook, but has been made open source for some
time now, and it’s a higher-level abstraction of the Hadoop framework that
allows anyone to make queries against data stored in a Hadoop cluster just as
if they were manipulating a conventional data store. It amplifies the reach of
Hadoop, making it more familiar for BI users.
PIG
PIG is another bridge that tries to
bring Hadoop closer to the realities of developers and business users, similar
to Hive. Unlike Hive, however, PIG consists of a “Perl-like” language that
allows for query execution over data stored on a Hadoop cluster, instead of a
“SQL-like” language. PIG was developed by Yahoo!, and, just like Hive, has also
been made fully open source.
WibiData
WibiData is a combination of web
analytics with Hadoop, being built on top of HBase, which is itself a database
layer on top of Hadoop. It allows web sites to better explore and work with
their user data, enabling real-time responses to user behavior, such as serving
personalized content, recommendations and decisions.
PLATFORA
Perhaps the greatest limitation of
Hadoop is that it is a very low-level implementation of MapReduce, requiring
extensive developer knowledge to operate. Between preparing, testing and
running jobs, a full cycle can take hours, eliminating the interactivity that
users enjoyed with conventional databases. PLATFORA is a platform that turns
user’s queries into Hadoop jobs automatically, thus creating an abstraction
layer that anyone can exploit to simplify and organize datasets stored in
Hadoop.
Storage Technologies
As the data volumes grow, so does
the need for efficient and effective storage techniques. The main evolutions in
this space are related to data compression and storage virtualization.
SkyTree
SkyTree is a high-performance
machine learning and data analytics platform focused specifically on handling
Big Data. Machine learning, in turn, is an essential part of Big Data, since
the massive data volumes make manual exploration, or even conventional
automated exploration methods unfeasible or too expensive.
Big
Data in the cloud
As we can see, from Dr. Kaur’s
roundup above, most, if not all, of these technologies are closely associated with
the cloud. Most cloud vendors are already offering hosted Hadoop clusters that
can be scaled on demand according to their user’s needs. Also, many of the
products and platforms mentioned are either entirely cloud-based or have cloud
versions themselves.
Big Data and cloud computing go
hand-in-hand. Cloud computing enables companies of all sizes to get more value
from their data than ever before, by enabling blazing-fast analytics at a
fraction of previous costs. This, in turn drives companies to acquire and store
even more data, creating more need for processing power and driving a virtuous
circle.
A visualization created by IBM of Wikipedia edits. At
multiple terabytes in
size, the text and images of Wikipedia are a classic example of big data.
Big
data is a collection of data sets so
large and complex that it becomes difficult to process using on-hand database
management tools or traditional data processing applications.
The
challenges include capture, curation, storage, search, sharing, transfer,
analysis, and visualization. The trend to larger data sets is due to the
additional information derivable from analysis of a single large set of related
data, as compared to separate smaller sets with the same total amount of data,
allowing correlations to be found to "spot business trends, determine
quality of research, prevent diseases, link legal citations, combat crime, and determine
real-time roadway traffic conditions."
As
of 2012[update],
limits on the size of data sets that are feasible to process in a reasonable
amount of time were on the order of exabytes of
data.
Data
sets grow in size in part because they are increasingly being gathered by ubiquitous
information-sensing mobile devices, aerial sensory technologies (remote sensing),
software logs, cameras, microphones, radio-frequency identification
readers, and wireless sensor networks.
The
world's technological per-capita capacity to store information has roughly
doubled every 40 months since the 1980s; as of 2012[update], every day 2.5 quintillion (2.5×1018) bytes of data were created.
The
challenge for large enterprises is determining who should own big data
initiatives that straddle the entire organization.
Big
data is difficult to work with using most relational database management
systems and desktop statistics and visualization packages, requiring instead
"massively parallel software running on tens, hundreds, or even thousands
of servers".
What
is considered "big data" varies depending on the capabilities of the
organization managing the set, and on the capabilities of the applications that
are traditionally used to process and analyze the data set in its domain.
"For some organizations, facing hundreds of gigabytes of data for the
first time may trigger a need to reconsider data management options. For
others, it may take tens or hundreds of terabytes before data size becomes a
significant consideration."
Definition
Big
data usually includes data sets with sizes beyond the ability of commonly used
software tools to capture, curate,
manage, and process the data within a tolerable elapsed time. Big data sizes
are a constantly moving target, as of 2012[update]
ranging from a few dozen terabytes to many petabytes of
data in a single data set. The target moves due to constant improvement in
traditional DBMS technology as well as new databases like NoSQL and their ability to handle larger
amounts of data. With this difficulty, new platforms of "big data"
tools are being developed to handle various aspects of large quantities of
data.
In
a 2001 research report and related lectures, META Group
(now Gartner)
analyst Doug Laney defined data growth challenges and opportunities as being
three-dimensional, i.e. increasing volume (amount of data), velocity (speed of
data in and out), and variety (range of data types and sources). Gartner, and
now much of the industry, continue to use this "3Vs" model for
describing big data.
In
2012, Gartner
updated its definition as follows: "Big data are high volume, high
velocity, and/or high variety information assets that require new forms of
processing to enable enhanced decision making, insight discovery and process
optimization."
Additionally,
a new V "Veracity" is added by some organizations to describe it.
If Gartner’s definition (the 3Vs) is
still widely used, the growing maturity of the concept fosters a more sound
difference between Big Data and Business Intelligence, regarding data and their
use:
·Business
Intelligence uses descriptive statistics with data with high
information density to measure things, detect trends etc.;
·Big
Data uses inductive
statistics with data with low information density whose huge volume
allow to infer laws and thus givingto
Big Data some predictive capabilities.
Examples
Examples include Big Science, RFID, sensor networks, social networks,
big social data analysis (due to the social data revolution), Internet documents,
Internet search indexing, call detail records, astronomy, atmospheric science,
genomics, biogeochemical, biological, and other complex and often
interdisciplinary scientific research, military surveillance, forecasting drive
times for new home buyers, medical records, photography archives, video
archives, and large-scale e-commerce.
Big science
The Large Hadron Collider experiments represent about 150
million sensors delivering data 40 million times per second.
There are nearly 600 million
collisions per second. After filtering and refraining from recording more than
99.999% of these streams, there are 100 collisions of interest per second.
As
a result, only working with less than 0.001% of the sensor stream data, the
data flow from all four LHC experiments represents 25 petabytes annual rate
before replication (as of 2012). This becomes nearly 200 petabytes after
replication.
·If
all sensor data were to be recorded in LHC, the data flow would be extremely
hard to work with. The data flow would exceed 150 million petabytes annual rate,
or nearly 500 exabytes
per day, before replication. To put the number in perspective, this is
equivalent to 500 quintillion
(5×1020) bytes per day, almost 200 times higher than all the other
sources combined in the world.
Science
and research
When the Sloan Digital Sky Survey (SDSS) began collecting
astronomical data in 2000, it amassed more in its first few weeks than all data
collected in the history of astronomy. Continuing at a rate of about 200 GB per
night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope,
successor to SDSS, comes online in 2016 it is anticipated to acquire that
amount of data every five days.
Decoding the human genome originally took 10 years to
process, now it can be achieved in less than a week : the DNA sequencers
have divided the sequencing cost by
10 000 in the last ten years,
which is a factor 100 compared to Moore's Law.
Computational social science — Tobias Preiset
al. used Google
Trends data to demonstrate that Internet users from countries with
a higher per capita gross domestic product (GDP) are more likely to search for
information about the future than information about the past. The findings
suggest there may be a link between online behaviour and real-world economic
indicators. The authors of the study examined Google queries logs made by
Internet users in 45 different countries in 2010 and calculated the ratio of
the volume of searches for the coming year (‘2011’) to the volume of searches
for the previous year (‘2009’), which they call the ‘future orientation index’. They compared the future
orientation index to the per capita GDP of each country and found a strong
tendency for countries in which Google users enquire more about the future to
exhibit a higher GDP. The results hint that there may potentially be a
relationship between the economic success of a country and the
information-seeking behavior of its citizens captured in big data.
In 2012, the Obama administration announced the Big Data
Research and Development Initiative, which explored how big data could be used
to address important problems facing the government. The initiative was
composed of 84 different big data programs spread across six departments.
·When
finished, the facility will be able to handle yottabytes of
information collected by the NSA over the Internet.
Private
sector
·Amazon.com
handles millions of back-end operations every day, as well as queries from more
than half a million third-party sellers. The core technology that keeps Amazon
running is Linux-based and as of 2005 they had the world’s three largest Linux
databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.
·Walmart handles
more than 1 million customer transactions every hour, which is imported into
databases estimated to contain more than 2.5 petabytes (2560 terabytes) of
data – the equivalent of 167 times the information contained in all the
books in the US Library of Congress.
·Facebook
handles 50 billion photos from its user base.
·FICO Falcon Credit Card Fraud Detection System
protects 2.1 billion active accounts world-wide.
·The
volume of business data worldwide, across all companies, doubles every 1.2
years, according to estimates.
·Windermere Real Estate uses anonymous GPS signals
from nearly 100 million drivers to help new home buyers determine their typical
drive times to and from work throughout various times of the day.
International
development
Following decades of work in the
area of the effective usage of information and communication
technologies for development (or ICT4D), it has been suggested that Big
Data can make important contributions to international development.
On the one hand, the advent of Big Data delivers the cost-effective prospect to
improve decision-making in critical development areas such as health care, employment, economic productivity, crime and security, and natural disaster
and resource management. On
the other hand, all the well-known concerns of the Big Data debate, such as privacy,
interoperability challenges, and the almighty power of imperfect algorithms,
are aggravated in developing countries by long-standing development challenges
like lacking technological infrastructure and economic and human resource
scarcity. "This has the potential to result in a new kind of digital divide: a
divide in data-based intelligence to inform decision-making."
Market
"Big data" has increased
the demand of information management specialists in that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC,
and HP
have spent more than $15 billion on software firms only specializing in data
management and analytics. In 2010, this industry on its own was worth more than
$100 billion and was growing at almost 10 percent a year: about twice as
fast as the software business as a whole.
Developed economies make increasing
use of data-intensive technologies. There are 4.6 billion mobile-phone
subscriptions worldwide and there are between 1 billion and 2 billion people
accessing the internet. Between 1990 and 2005, more than 1 billion people worldwide
entered the middle class which means more and more people who gain money will
become more literate which in turn leads to information growth.
The world's effective capacity to exchange information
through telecommunication networks
was
In 2004, Google published a paper on
a process called MapReduce
that used such an architecture. MapReduce framework provides a parallel
programming model and associated implementation to process huge amount of data.
With MapReduce, queries are split and distributed across parallel nodes and
processed in parallel (the Map step). The results are then gathered and
delivered (the Reduce step). The framework was incredibly successful, so others
wanted to replicate the algorithm. Therefore, an implementation of MapReduce
framework was adopted by an Apache open source project named Hadoop.
MIKE2.0 is
an open approach to information management. The methodology addresses handling
big data in terms of useful permutations of
data sources, complexity in
interrelationships, and difficulty in deleting (or modifying) individual
records.
Technologies
DARPA’s Topological Data Analysis program seeks the
fundamental structure of massive data sets.
Some but not all MPP relational
databases have the ability to store and manage petabytes of data. Implicit is
the ability to load, monitor, back up, and optimize the use of the large data
tables in the RDBMS.
DARPA’s Topological Data Analysis
program seeks the fundamental structure of massive data sets and in 2008 the
technology went public with the launch of a company called Ayasdi.
The practitioners of big data
analytics processes are generally hostile to slower shared storage, preferring
direct-attached storage (DAS) in its various forms from solid state disk (SSD)
to high capacity SATA disk buried inside parallel processing nodes. The
perception of shared storage architectures—SAN and NAS—is that they are
relatively slow, complex, and expensive. These qualities are not consistent
with big data analytics systems that thrive on system performance, commodity
infrastructure, and low cost.
Real or near-real time information
delivery is one of the defining characteristics of big data analytics. Latency
is therefore avoided whenever and wherever possible. Data in memory is
good—data on spinning disk at the other end of a FC SAN connection is not. The
cost of a SAN at the scale needed for analytics applications is very much
higher than other storage techniques.
There are advantages as well as
disadvantages to shared storage in big data analytics, but big data analytics
practitioners as of 2011[update]
did not favour it.
Research
activities
In March 2012, The White House
announced a national "Big Data Initiative" that consisted of six
Federal departments and agencies committing more than $200 million to big data
research projects.
The initiative included a National
Science Foundation "Expeditions in Computing" grant of $10 million
over 5 years to the AMPLab at the University of California, Berkeley. The
AMPLab also received funds from DARPA, and over a dozen industrial sponsors and
uses big data to attack a wide range of problems from predicting traffic
congestion] to
fighting cancer.
The White House Big Data Initiative
also included a commitment by the Department of Energy to provide $25 million
in funding over 5 years to establish the Scalable Data Management, Analysis and
Visualization (SDAV) Institute, led by the Energy Department’s Lawrence Berkeley National Laboratory.
The SDAV Institute aims to bring together the expertise of six national
laboratories and seven universities to develop new tools to help scientists
manage and visualize data on the Department’s supercomputers.
The U.S. state of Massachusetts
announced the Massachusetts Big Data Initiative in May 2012, which provides
funding from the state government and private companies to a variety of
research institutions. The Massachusetts Institute of Technology
hosts the Intel Science and Technology Center for Big Data in the MIT Computer Science and
Artificial Intelligence Laboratory, combining government,
corporate, and institutional funding and research efforts.
The European Commission is funding a
2-year-long Big Data Public Private Forum
through their Seventh Framework Program to
engage companies, academics and other stakeholders in discussing Big Data
issues. The project aims to define a strategy in terms of research and
innovation to guide supporting actions from the European Commission in the
successful implementation of the Big Data economy. Outcomes of this project
will be used as input for Horizon 2020,
their next framework program.
The IBM sponsored 37th annual
"Battle of the Brains" student Big Data championship will be held in
July 2013. The inaugural professional 2014 Big Data World Championship is to be
held in Dallas, Texas.
Critique
Critiques of the Big Data paradigm
come in two flavors, those that question the implications of the approach
itself, and those that question the way it is currently done.
Critiques
of the Big Data paradigm
"A crucial problem is that we
do not know much about the underlying empirical micro-processes that lead to
the emergence of the[se] typical network characteristics of Big Data". In
their critique, Snijders, Matzat, and Reips
point out that often very strong assumptions are made about mathematical
properties that may not at all reflect what is really going on at the level of
micro-processes. Mark Graham has leveled broad critiques at Chris Anderson's assertion that big data will
spell the end of theory: focusing in particular on the notion that big data
will always need to be contextualized in their social, economic and political
contexts. Even as companies invest eight- and nine-figure sums to derive insight
from information streaming in from suppliers and customers, less than 40% of employees
have sufficiently mature processes and skills to do so. To overcome this
insight deficit, "big data", no matter how comprehensive or well
analyzed, needs to be complemented by "big judgment", according to an
article in the Harvard Business Review.
Much in the same line, it has been
pointed out that the decisions based on the analysis of Big Data are inevitably
"informed by the world as it was in the past, or, at best, as it currently
is". Fed by a large number of data on past experiences, algorithms can
predict future development if the future is similar to the past. If the systems
dynamics of the future change, the past can say little about the future. For
this, it would be necessary to have a thorough understanding of the systems
dynamic, which implies theory. As a response to this critique it has been
suggested to combine Big Data approaches with computer simulations, such as agent-based models,
for example. Those are increasingly getting
better in predicting the outcome of social complexities of even unknown future
scenarios through computer simulations that are based on a collection of
mutually interdependent algorithms.
In
addition, use of multivariate methods that probe for the latent structure of
the data, such as factor
analysis and cluster analysis,
have proven useful as analytic approaches that go well beyond the bi-variate
approaches (cross-tabs) typically employed with smaller data sets.
In Health and biology, conventional
scientific approaches are based on experimentation. For these approaches, the
limiting factor are the relevant data that can confirm or refute the initial
hypothesis. A new postulate is accepted now in biosciences : the
information provided by the data in huge volumes (omics) without prior hypothesis is
complementary and sometimes necessary to conventional approaches based on
experimentation. In the massive approaches it is the formulation of a relevant
hypothesis to explain the data that is the limiting factor. The search logic is
reversed and the limits of induction ("Glory of Science and Philosophy
scandal", C. D.
Broad, 1926) to be considered.
Consumer privacy
advocates are concerned about the threat to privacy represented by increasing
storage and integration of personally identifiable information;
expert panels have released various policy recommendations to conform practice
to expectations of privacy.
Critiques
of Big Data execution
Danah Boyd
has raised concerns about the use of big data in science
neglecting principles such as choosing a representative sample by being too concerned about
actually handling the huge amounts of data. This approach may lead to results bias in one way or another. Integration
across heterogeneous data resources — some that might be considered
"big data" and others not — presents formidable logistical as
well as analytical challenges, but many researchers argue that such
integrations are likely to represent the most promising new frontiers in
science.
