Big Data Analytics and Predictive Analytics - _ Predictive Analytics Today
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11/12/ 2014 Big data Anal yti cs and Predi cti ve Anal yti cs - | Predictive Analytics Today ht tp: //www.predictiveanalyticstoday.com/big-data-analytics-and-predi ct i ve-analytics/ 1/ 9 Predictive Analytics » Business Intelligence » Text Analytics » Bigdata » Interview News » Events 4.73/5 (94.55%) 33 ratings Gartner added big data to its 2011 hype cycle and has called it one of the top 10 strategic technologies for 2012, stating, “The size, complexity of formats and speed of delivery exceeds the capabilities of traditional data management technologies; it requires the use of new or exotic technologies simply to manage the volume alone”. Big data Analytics and Predictive Analytics Data is emerging as the world’s newest resource for competitive advantage among nations, organizations and business. It is estimated that every day we create 2.5 quintillion bytes of data from a variety of sources. These are from the computer notes to posts on social media sites and from purchase transaction records to pictures. These collection of data sets which are so large and complex and are difficult to process using the on hand database management tools are known as Big data. The challenges in Big data includes capture, curation, storage, search, sharing, transfer, analysis and visualization of the data. Big data has few key characteristics such as volume, sources, velocity, variety and veracity. The first among these is volume. Experts predict that by 2020, the volume of data in the world will grow to 40 Zettabytes. This affects every business, governments and individual. Based on a recent study,2.8 Zettabytes of data were created in 2012 and only .5% of that data were used for analysis. Unstructured data, such as texts, notes, logs makes up a large chunk of this data volume and these requires text mining to analyze the data. The business data is also growing at these same exponential rate too.Along with the volume, the number of sources, from where the data is extracted are also growing. Data is increasingly accelerating the velocity at which it is created, as the process are moved from batch to a real time business. The demands of the business from these data also has increased, from an answer next week to an answer in a minute. Home Bigdata Big data Analytics and Predictive Analytics Foll ow Pr ed ic ti Search S ea rc h P re dic ti Subscribe to An alyt ic s T od Email address GO ! Recent From S To Qu An Sof Big data nalytics and Predictive nalytics Top 30 Predi Software A
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Big data Analytics Technology
1.MapReduce
MapReduce was created by Google in 2004. It is a model inspired by the map and reduce functions for
processing large data sets with a parallel, distributed algorithm on a cluster.
2.Hadoop
Hadoop is an open source Apache implementation project. It was created by Yahoo in 2004 as a way to
implement the MapReduce function. Hadoop enables applications to work with huge amounts of data stored on
various servers. Hadoop has a large scale file system which is known as Hadoop Distributed File System or HDFS
and this can write programs, manages the distribution of programs, accepts the results, and then generates a
data result set.
3.In memory database
Data in main memory can be accessed faster than data stored in hard disk or other flash storage device. A
database management system that primarily relies on main memory for computer data storage is called an In
memory database.
4.Massively parallel processing databases
Massively parallel processing is a loosely coupled databases where each server or node have memory or
processors to process data locally and data is partitioned across multiple servers or nodes.
5.Search based applications
Search based applications are search engine platform is used to aggregate and classify data and use natural
language technologies for accessing the data.
6.Data mining grids
Data mining grids are environment which uses grid computing concepts, which allows to integrate data from
various online and remote data sources.
7.Distributed file systems
Distributed file system is a shared file system which is shared by being simultaneously mounted on multiple
servers.
8.Distributed databases
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Distributed databases is a database system which is controlled by a distributed database management system.
9.Cloud
Cloud computing is distributed computing over a network.
Business benefits of Big data Analytics
March towards business goals faster by turning dormant data into new opportunities making use of big data
analytics.
Intuitively design very complex predictive models using casual factors
Big Data integration capabilities with traditional databases and other systems.
Hadoop Distributed File System for faster ‘reading from’ and ‘loading to’ performance and scalability.
Wide range of Big data applications and analytics to analyse more history data.
Visualize, discover, and share hidden insights for forward looking plan.
From adhoc report analysis to Real-time answers using Big data.
Linguistic analysis and extracts relevant content from files, Web logs and social media.
Data from Multiple sources analysed for one business solution.
Real time answers from unstructred data.
Big data Analytics and Predictive Analytics
Big data and Predictive Analytics processing
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► Web Data ► Hadoop Data ► Data Base ► SAS
Provide Alert when market share for my products are dropping in specific regions.
Where and what was the Rx trend and what predictions are there for future ?
Which products and product groups are our best and worst? Used By Which regions? and what is the
percentage cummulative decline ?
How much commission did the sales folks accumulate ?
What are a few planned scenarios moving forward ?
How do I leverage the past to segment regions to concentrate to reduce the drop moving forward?
Based on previous Rx, what clusters of regions should I market to?
What’s the word on the street? How will the digital media help me target new regions and what is going to be
my marketing effectiveness ?
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► Text Analytics ► Data Warehouse ► Data Visualizer ► Analysis Software
Author: PAT
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Bhaskar
DECEMBER 3, 2013
This was a very interesting read.I would like to see more on this topic.
Nicholas Merolla
DECEMBER 24, 2013
I too think this was interesting reading; it covered many of the salient points of Big Data Analytics.
There are a couple of items, I respectfully submit that the author did not address [although they
may be addressed in the links provided,] and which I’d like to see addressed at some point. To wit:
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§ There is not any space allotted in the literature to address the management requirements of
hyper-large clusters, and from what I’ve read; I don’t see vendors offering any products that speak to
the point. Specifically; in a Hadoop cluster [for example] with perhaps 100s of nodes; what is the
impact on management of that cluster’s processing capacity and operations staff? Likewise, with a
large enough cluster, one can reasonably expect to have a downed node almost consistently. Thus
far, no vender that I’ve researched, except perhaps one, offers a single system image and
automatically accounts for recovery from downed nodes at the OS layer. Moreover the expectation
is that fail-over processing in reaction to a failed node condition will undoubtedly burden the cluster
with the additional processing burden on the cluster if that processing is not done at the OS layer.
What then happens to expected performance expectations [or agreements.]
§ None of the vendors claiming to be in the Big Data space are taking on the problem of zero [or
very low] data latency. Gartner, I believe, published a report on Zero Latency Enterprises [ZLE] in a
paper a number of years ago, but no one today save for SAP, and they vaguely refer to ZLE, has
taken on the requirement of ZLE or very low latency [VLLE.] It does an enterprise little good to claim
predictive analysis and real-time monitoring capabilities via a DW unless the ZLE issue is tackled
head on. I’d like to see the authors [Gartner, perhaps] reintroduce this requirement as it applies to
Big Data Predictive Analytics.
§ As regards to in-memory data bases, it does a DW owner little good to have an in-memory data
base if that owner is always looking at stale information. A few examples come to mind: capturing a
customer before they’ve left the store in retail; real time fraud detection in credit card processing as
offered in a comForte paper by comparing card transactions with something seemingly as
insignificant as a Tweet.
§ I expect that ZLE will become part of the price of entry into the arena as data volumes continue to
grow. To perform an ETL activity off-line in a batch or parallel batch mode won’t cut the mustard
until someone figures out how to get more than 24 hours into a day. DW/BI vendors have to start
offering or, at least, showing on their product road maps how they address the issue of ZLE or VLLE
in an interoperable, heterogeneous environment.
Post a Reply
Vrej JANUARY 30, 2014
Fantastic
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