DALLAS, Aug. 21, 2014 /PRNewswire-iReach/ -- Amid the proliferation of real
time data from sources such as mobile devices, web, social media, sensors,
log files and transactional applications, Big Data has found a host of
vertical market applications, ranging from fraud detection to R&D.
Photo - http://photos.prnewswire.com/prnh/20140821/138541
"Big Data Market: 2014 – 2020 – Opportunities, Challenges, Strategies,
Industry Verticals & Forecasts"
In 2014 Big Data vendors will pocket nearly $30 Billion from hardware,
software and professional services revenues Big Data investments are further
expected to grow at a CAGR of nearly 17% over the next 6 years, eventually
accounting for $76 Billion by the end of 2020 The market is ripe for
acquisitions of pure-play Big Data startups, as competition heats up between
IT incumbents Nearly every large scale IT ven... (more)
What do you get when you combine Big Data technologies….like Pig and
Hive? A flying pig?
No, you get a “Logical Data Warehouse”.
My general prediction is that Cloudera and Hortonworks are both aggressively
moving to fulfilling a vision which looks a lot like Gartner’s “Logical
Data Warehouse”….namely, “the next-generation data warehouse that
improves agility, enables innovation and responds more efficiently to
changing business requirements.”
In 2012, Infochimps (now CSC) leveraged its early use of stream processing,
NoSQLs, and Hadoop to create a design pattern which combined real-time,
ad-hoc, and batch analytics. This concept of combining the best-in-breed Big
Data technologies will continue to advance across the industry until the
entire legacy (and proprietary) data infrastructure stack will be replaced
with a new (and open) one.
As this is happening, I predi... (more)
As the new year begins, global companies face the coming year's most
prominent IT and business challenge: Big Data.
The focus for IT will be to provide high performance analytics capabilities
at the lowest cost, as business users need to tap into volumes of
multi-structured data about their customers and markets to gain competitive
RainStor, a provider of Big Data management software, has released five
predictions focused on how enterprise Big Data will affect organizations in
2012. Based on client and partner experience, market research and
conversations with industry experts, here are RainStor's five predictions for
Big Data in 2012:
Prediction #1: Big Data will Transition from Technology "Buzz" to a Real
Business Challenge Affecting Many Large Global Enterprises
Big Data is largely centered on leveraging the open source Apache Hadoop
analytics platform... (more)
For many years, companies collected data from various sources that often
found its way into relational databases like Oracle and MySQL. However, the
rise of the Internet, Web 2.0, and recently social media began an enormous
increase in the amount of data created as well as in the type of data. No
longer was data relegated to types that easily fit into standard data fields.
Instead, it now came in the form of photos, geographic information, chats,
Twitter feeds, and emails. The age of Big Data is upon us.
Big Data Beginnings
A study by IDC titled "The Digital Universe Decade" projects a 45-fold
increase in annual data by 2020. In 2010, the amount of digital information
was 1.2 zettabytes (1 zettabyte equals 1 trillion gigabytes). To put that in
perspective, the equivalent of 1.2 zettabytes is a full-length episode of
"24" running continuously for 125 million years, ac... (more)
Back when we were doing DB2 at IBM, there was an important older product
called IMS which brought significant revenue. With another database product
coming (based on relational technology), IBM did not want any cannibalization
of the existing revenue stream. Hence we coined the phrase “dual database
strategy” to justify the need for both DBMS products. In a similar vain,
several vendors are concocting all kinds of terms and strategies to justify
newer products under the banner of Big Data.
One such phrase is Fast Data. We all know the 3Vs associated with the term
Big Data – volume, velocity and variety. It is the middle V (velocity) that
says data is not static, but is changing fast, like stock market data,
satellite feeds, even sensor data coming from smart meters or an aircraft
engine. The question always has been how to deal with such type of changing
data (as ... (more)
On Monday, December 5, Bob Gourley went on the Enterprise CIO Forum to
explain Big Data and why it matters. First, he defined Big Data simply as the
data your organization cannot currently analyze. Though some technologists
give more precise definitions, this sums up the challenge enterprises now
face. If you can deal with all of your data now, you don’t have a Big Data
problem, but as soon as you have more data than you can effectively manage to
finding the answers you need fast enough to use them, you need a Big Data
solution. Structured data and relational databases can also be Big Data but
what we’re really talking about is the type and volume of information that
exceeds traditional methods. New solutions include MapReduce, originally
developed at Google to analyze and index the entire Internet, and Hadoop
which grew to use those new methods.
We see Big Data so... (more)
The good news about the Big Data market is that we generally all agree on the
definition of Big Data, which has come to be known as data that has volume,
velocity and variety where businesses need to collect, store, manage and
analyze in order to derive business value or otherwise known as the "4 V's."
However, the problem with such a broad definition is that it can mean
different things to different people once you start to put some real values
next to those V's.
Let's be honest, Volume can be a different thing to different organizations.
To some it is anything above 10 terabytes of managed data in their BI
environment and to others it is petabyte scale and nothing less. Likewise
velocity can be multi-billions of daily records coming into the enterprise
from various external and internal networks. When it really comes down to it,
each business situation will be qu... (more)
In the past few weeks I visited several Cloud and Big Data conferences that
provided me with a lot of insight. Some people only consider the technology
side of Big Data technologies like Hadoop or Cassandra. The real driver
however is a different one. Business analysts have discovered Big Data
technologies as a way to leverage tons of existing data and ask questions
about customer behavior and all sorts relationships to drive business
strategy. By doing that they are pushing their IT departments to run ever
bigger Hadoop environments and ever faster real-time systems.
What's interesting from a technical side is that ad-hoc analytics on existing
data is allowed to take some time. However ad-hoc implies people waiting for
an answer, meaning we are talking about minutes and not hours. Another
interesting insight is that Hadoop environments are never static or
What changes in the cloud computing and big data landscape should we be
expecting in 2013?
In this article we offer a round-up of industry experts' opinions as they
were asked by Cloud Expo / BigDataExpo Conference Chair Jeremy Geelan to
preview the fast-approaching year ahead.
2013 Will Be The Year of Big Data | The Internet of Things | Cloud To The
Rescue (DR) | SSD
John Engates | @jengates
CTO of Rackspace Hosting
Now its CTO, John joined Rackspace in August 2000, just a year after the
company was founded, as VP of Operations, managing the datacenter operations
and customer-service teams. Two years later, when Rackspace decided to add
new services for larger enterprise customers, he created and helped develop
the Intensive Hosting business unit. Most recently, he has played an active
role in the evolution and evangelism of Rackspace's cloud computing strategy
Today, there are two main ways to use Hadoop with R and big data:
1. Use the open-source rmr package to write map-reduce tasks in R (running
within the Hadoop cluster - great for data distillation!)
2. Import data from Hadoop to a server running Revolution R Enterprise,
via Hbase, ODBC (for high-performance Hadoop/SQL interfaces), or streaming
data direct from HDFS to ScaleR's big-data predictive algorithms.
And now, there are even more Hadoop platforms supported for use with
Revolution R Enterprise. You can use:
Cloudera CDH3 or CDH4 IBM BigInsights 2 New! Hortonworks Data Platform 1.2
New! Intel's Distribution for Hadoop (announced today)
And by the end of the year, there will be a third way to use Hadoop with R:
3. Leave the data in Hadoop, and use ScaleR's "in-Hadoop predictive
We announced today that we are jointly developing in-Hadoop predictive
BigData (and Hadoop) are buzzword and growth areas of computing; this article
will distill the concepts into easy-to-understand terms.
As the name implies, BigData is literally "big data" or "lots of data" that
needs to be processed. Lets take a simple example: the city council of San
Francisco is required to take a census of its population - literally how many
people live at each address. There are city employees who are employed to
count the residents. The city of Los Angeles has a similar requirement.
Consider are two methods to accomplish this task:
1. Request all the San Francisco residents to line up at City Hall and be
prcessed by the city employees. Of course, this is very cumbersome and time
consuming because the people are brought to the city hall and processed one
by one - in scientific terms the data are transfered to the processing node.
The people have ... (more)