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“Big Data” is an emerging & continues to be the topic of much discussion and hype.There are some staunch believers of this and there are some who dismiss this as a bubble destined to subside. Companies such as facebook and amazon are already using this. Lets try to understand this whole thing.
WHY BIG DATA?
Take a look on statistics first for Big Data
- 50 billion web pages
- 800 million Facebook users
- 1000 million Facebook pages
- 200 million Twitter accounts
- 100 million tweets per day
- 5 billion Google queries per day
- Millions of servers, Petabytes of data
Varieties of Data
- Video / Audio
- Images / Pictures
- Diverse internal and external data
Sources of Data
- News / Feeds / Blogs / forums
- Groups / Polls / Chats / Wiki
That means world today is generating data at a frenetic pace. Statistics are available on the net that the data is doubled in every X days. The good thing about this data is that it is available and potentially contains useful information. What’s not so good is how to process this information and use it effectively.
The market place has become competitive and will be becoming more in years to come. The key to growth (and survival) is innovation. Big Data can help in uncover the strategies which can help in making the difference. Big Data though, is not a “magic wand” or “one size fits all” approach, rather it will require careful strategising and planning to achieve something tangible. The following sections will discuss the various facets.
Big data can be defined as data which has:(Four V’s of Big Data Consideration)
- variety and
The world is producing data with great velocity.Fact reported by IDC is Worldwide digital content will double in 18 months, and every 18 months thereafter.
We heard nowadays that petabytes do not amaze us anymore. As per The Economist In 2005, humankind created 150 exabytes of information. In 2011, 1,200 exabytes were be created
The data is varied today, as well, e.g. structured, semi structured and complex. As Gartner, 80% of enterprise data will be unstructured, spanning traditional and non traditional sources
The fourth one, value, is not so straightforward. The value is something whose unlocking is important.
THE BIG DATA DIFFERENCE
Big data is like traditional data in many ways: It must be captured, stored, organized, and analyzed, and the results of the analysis need to be integrated into established processes and influence how the business operates.
But because big data comes from relatively new types of data sources that previously weren’t mined for insight, companies aren’t accustomed to collecting information from these sources, nor are they used to dealing with such large volumes of unstructured data. Therefore, much of the information available to enterprises isn’t captured or stored for long-term analysis, and opportunities for gaining insight are missed.
USES OF BIG DATA
Typical Example of Big data usage are :
- Log Analytics & Storage
- Smart Grid / Smarter Utilities
- RFID Tracking & Analytics
- Fraud / Risk Management & Modeling
- 360Â° View of the Customer
- Warehouse Extension
- Email / Call Center Transcript Analysis
- Call Detail Record Analysis
WHICH KIND OF INDUSTRY IS DRIVERS FOR BIG DATA
- Life science
CHALLENGE & OPPORTUNITY
Analysis of big data including new types of data that haven’t been analyzed before , challange is to provides a deeper level of insight into what customers are thinking and how the business operates. The potential payoffs are improving customer retention, selling individual customers more products, and producing items with higher quality and lower rates of return. Studies show that, with proper use, big data can really improve the bottom line to make an impact on overall profitability.
Organizations can well today with Big Data if they succeed in defining
- What they want
- They understand about the data they need
- They understand the data that they have which can be used.
- They know where to look for the data which they need.
The first and foremost on the list is defining the problem. Once you have this then the next stage of understanding and getting data can be achieved. This requires investment, intellectual, financial and time.
The next stage is analysis once you have your data with you. This analysis should be something which can aid you invgetting what you want and help you with the business.
BIG DATA VENDORS
Big Data is a $70 billion industry and growing at a rapid rate of 15% to 20% a year. In our view, this helps put to rest the notion that ‘Big Data’ is just a fanciful marketing term. Big Data developments will be perhaps the most critical new marketplace for storage solutions providers in the coming decade. [Reference EMC]
|Big Data Technology||Example Vendors|
|Big Data file and Database||
Appistry,Basho, Google, Hadoop (Hartonworks,Cloudera,MapR) ,LexisNexis,VMware, Microsoft, Aster Data
|Big data Analytics||Appistry,Aster Data, Hadoop (Hartonworks,Cloudera,MapR),IBM, LexisNexis,Karmasphere|
|Big Data Integration||Appistry,Aster Data,Composite,Hadoop Pig and Hive, informatica, LexisNexis|
|DW applicance with big data Integration||EMC/Greenplum, IBM/Netezza,TeraData/Aster Data|
|Trandational BI with Big data Integration Options||Eddeca, Pentaho,MicroStrategy, Tableau software|
|Stream processing and analysis||Apache S4, IBM, SAP, Tibco , Progress|
ORACLE & BIG DATA
Oracle offers integrated enterprise-ready big data platform, which includes the Oracle Big Data Appliance, Oracle Big Data Connectors, Oracle Exadata and Oracle Exalytics In-Memory Machine. By integrating and optimizing the technologies needed to acquire, organize, analyze and decide on big data, Oracle has made it very easy to jumpstart and maintain big data projects.
The World Economic Forum recently declared big data as an asset class.
What are the implications of making better sense of large amounts of unstructured data to uncover business opportunities, strategies, and more.
Is it really an emerging market with lots of innovation, startups, job creation? Or hype. The facts show penetration within the largest FMCG Consumer companies and Social Networks.
Why is it suddenly possible? What are the technical and organizational obstacles, and the most promising areas of opportunity?
What is Oracle doing?
How could we link Oracle Oracle Advanced Analytics BI with HADOOP?
Herewith look forward to start a discussion about BIG DATA .