Tuesday, 14 May 2013

How to tackle the 4V’s with Big Data


Each day, millions of bytes of data are created owing to the transactions carried on the internet. Every day, the Ecommerce industry rises and companies are using newer technologies to make the websites more user-friendly. The upcoming generation believes in plastic money, and the transactional data in the Ecommerce industry rises tremendously. To support the transactions, the companies need associated processes, which cater the demands of the customers and ensure the smooth functioning of the company. Hence, a tremendous amount of data happens to be generated on a daily basis in any company.

Based on private research, it is found that different data produced needs to be managed, and the Data Management system needs to follow  certain criteria. Big Data is the latest ground-breaking invention to tackle huge amounts of data. The following criteria is required to evaluate any Database management system and Big Data efficiently meets it.

1st V: Volume
Enterprises are growing with a huge amount of data. The integration of social media platforms has poured in ‘Tweets’ and ‘Updates’ worth a few Terabytes of data. People post reviews of products on blogs and other media, and eventually everything ends up coming in the company. Even service and utility companies like Power companies have to capture meter readings from a number of people and the subscription list rises every day. To tackle this huge amount of data, Big Data has proposed the revolutionary ‘No-SQL database’. This technology stores data as it comes without losing any of it. It offers the minimum delay, unlike its Relational counterparts.
2nd V: Velocity
Every now and then reports are required, and the executives require information to create them. Due to different departments in the company, the executives query different sections of database and information needs to be retrieved speedily. Big Data employs No-SQL technology like ‘Hadoop’, which does not store data in tables. The data gets stored in clusters and based on requirements you can retrieve the data from the cluster and meet your requirements. As entire database need not be scanned, the velocity of the system is considerably higher.

3rd V: Variety
Different sections of the company have different processes and each have different attributes. Sometimes, data needs to be passed and maintained across different departments. Big Data stores data in clusters and is not defined by Entity-Relationship parameters. Hence, it allows the storage of complex data like multimedia without any trouble. Thus, it is known to manage a variety of data.

4th V: Veracity
The management class does not trust information, if it is just from a single source. Big Data can aggregate data from diverse sources and improves the credibility of information. Thus, it promotes the veracity of data in an effective way.

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Monday, 6 May 2013

Big Data Analytics in action in the Telco industry.



Source: http://www-01.ibm.com/software/data/bigdata/

Deepak Rangarao, IBM Client Technical Specialist, takes us through a demo showing Big Data Analytics in action in the Telco industry. IBM's Big Data platform is at the heart of the solution that includes a real time dashboard and data mining from Netezza, InfoSphere Streams processing and scoring CDR's, BigInsights and ad-hoc analysis using BigSheets.

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Competitive Advantages of Big Data

Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes-even petabyte of information.
  •          Turn 12 terabytes of Tweets created each day into improved product sentiment analysis
  •          Convert 350 billion annual meter readings to better predict power consumption
Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.
  •          Scrutinize 5 million trade events created each day to identify potential fraud
  •          Analyze 500 million daily call detail records in real-time to predict customer churn faster
Variety: Big data is any type of data - structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together.
  •          Monitor 100’s of live video feeds from surveillance cameras to target points of interest
  •          Exploit the 80% data growth in images, video and documents to improve customer satisfaction
Veracity: 1 in 3 business leaders don’t trust the information they use to make decisions. How can you act upon information if you don’t trust it? Establishing trust in big data presents a huge challenge as the variety and number of sources grows.

Competitive Advantages of Big Data

Although Hadoop initially has been used by large web companies such as Google, Yahoo, Facebook for applications such as search engines, but its potential is much, much more. This report details what Hadoop is (and isn’t), brings into focus the key technologies and their interplay, provides a perspectives on different players, who’s doing what to productize them and how they fit into the ecosystem. It profiles the growing number of companies — from startups like MapR to Cloudera, the present leader in the space leveraging Hadoop plus 52 others – both announced and in stealth, to the strategies being adopted by Relational Database/Data Warehousing/ Business Intelligence/Data Integration incumbents like Oracle, IBM, Microsoft, SAP, Teradata, SAS, Microstrategy etc. to embrace the emerging technologies while new Big Data Infrastructure entrants the likes of EMC, NetApp, Cisco, Dell, Fujitsu, HP, Adobe and scores of others planning new products to address this space. . It charts out the SWOT analysis of both leaders and new suppliers as well as their competitive positioning and strategies. 



The report outlines how the market opportunities in Big Data are crystallizing to pick up serious steam while taking into account the challenges still hindering widespread adoption and where potential users can expect the market to go. It presents a 5 year market forecast 2010-15, market shares of leaders, likely M& A scenarios and examines go-to-market plans of leaders to provide big data solutions for several vertical industries such as financial services, healthcare and media. Finally it provides recommendation for vendors, channel players, end users and investors for timely play to leverage the opportunities presented by the emerging big data markets

To capitalize on the Big Data trend, a new breed of Big Data technologies (such as Hadoop and others) many companies have emerged which are leveraging new parallelized processing, commodity hardware, open source software and tools to capture and analyze these new data sets and provide a price/performance that is 10 times better than existing Database/Data Warehousing/Business Intelligence Systems. While most people would think of Google, Facebook as Media companies. In reality they are a myriad of other high growth internet oriented Big Data companies because the reality is that their businesses have been created due to their ability to effectively harness Big Data to their business advantage (e.g. Big Table from Google)


The report outlines how the market opportunities in Big Data are crystallizing to pick up serious steam while taking into account the challenges still hindering widespread adoption and where potential users can expect the market to go. It presents a 5 year market forecast 2010-15, market shares of leaders, likely M& A scenarios and examines go-to-market plans of leaders to provide big data solutions for several vertical industries such as financial services, healthcare and media. Finally it provides recommendation for vendors, channel players, end users and investors for timely play to leverage the opportunities presented by the emerging big data markets

To capitalize on the Big Data trend, a new breed of Big Data technologies (such as Hadoop and others) many companies have emerged which are leveraging new parallelized processing, commodity hardware, open source software and tools to capture and analyze these new data sets and provide a price/performance that is 10 times better than existing Database/Data Warehousing/Business Intelligence Systems. While most people would think of Google, Facebook as Media companies. In reality they are a myriad of other high growth internet oriented Big Data companies because the reality is that their businesses have been created due to their ability to effectively


With the explosion in the use of the Internet, vast majority of data growth is coming in the form of data sets that are not well suited for traditional relational database vendors like Oracle. Not only is the data too unstructured and/or too voluminous for a traditional RDBMS, the software and hardware costs required to crunch through these new data sets using traditional RDBMS technology are prohibitive. To solve this, new companies have emerged that through using new Big Data technologies, are leveraging commodity hardware and open source software - from data capture, operational integration, advanced analytics to visualization.




Is Big Data trouble for the Existing Database Order?

The economics inherent in using open source big data software running on commodity hardware will drive companies to consider these next generation systems when implementing new systems for new non transactional workloads. This is despite of being faced with the concomitant cost of developing expertise in the emerging alternatives. To illustrate this cost discrepancy in achieving high-performance solutions being provided today by traditional database vendors vs. the price points solutions put together using open source software and commodity hardware by the new generation big data vendors, three separate use cases illustrate the savings that are easily achieved as shown in the diagram as follows: (For details see chapter 3 of this report)

If RDBMS Could Handle Big Data Volumes, Why Bother? 

the economics inherent in using open source big data software running on commodity hardware will drive companies to consider these next generation systems when implementing new systems for new non transactional workloads, despite being faced with the cost of developing expertise in the emerging alternatives.

Leveraging the Competitive Advantages of Big Data

Much as Hadoop initially has been used by large web companies such as Google, Yahoo, Facebook for applications such as search engines, but its unrealized potential is huge.

Venture Investments Accelerated for Big Data 

Hadoop isn’t the only thing going in big data, but it’s driving the bus at this point and it seems to have a Midas touch: everything that touches it turns to gold. Hadoop-based startups have raised $104.5 million since May 2011. This is over and above $159.7 million raised since 2009 till May 2011 when Cloudera closed its first round. These totals don’t include the recent or unattributed rounds for Odiago, high investment for Yahoo spinoff Hortonworks (now adopted by Microsoft). In addition the NoSQL in their focus on unstructured data, also have announced just more than $90 million in funding overall. Not counted in the above numbers are: - Opera solutions $84M (Silverlake, JGE, Accel, KKR, Tola Capital) - Infineta $15M (Rembrandt Venture Partners, Alloy Ventures, North Bridge Venture Partners) - Xignite $10M (StarVest Partners, Spring Mountain Capital) - Platfora $5.7M (Andreessen Horowitz, In-Q-Tel) Never mind, the many analytic database vendors that play in the big data arena that have been acquired over the past couple years for billions in aggregate.

The Big Data 2011 Industry Report (updated in Feb 2012) brings into focus the key Database and Big Data technologies and their interplay, provides a perspectives on different players, who’s doing what to productize them and how they fit into the ecosystem. It profiles the growing number of companies — from startups like MapR to Cloudera, the present leader in the space leveraging Hadoop plus 104 others in full detail – both announced and in stealth, to the strategies being adopted by Relational Database/Data Warehousing/ Business Intelligence/Data Integration incumbents like Oracle, IBM, Microsoft, SAP, Teradata, SAS, Microstrategy etc. to embrace the emerging technologies. It outlines how new Big Data Infrastructure entrants the likes of EMC, NetApp, Cisco, Dell, Fujitsu, HP, Adobe and scores of others are planning new products to address this space. 

It charts out the SWOT analysis of leaders and new suppliers as well as their competitive positioning and strategies. The report lists the various operational business intelligence challenges companies face and provides guidance as to how enterprise IT leaders can harness the deeper insights provided by Big Data to manage complex operational problems and empower their management in knowledgeable decision-making to get the competitive advantage for their companies. 

For IT, the report lays out the rapidly evolving big data technology ecosystem - different big data technologies from Hadoop, Distributed File Systems, emerging NoSQL derivatives for implementation in private and hybrid cloud-based environments, Storage Infrastructure Requirements to Store, Access, Secure, Prepare for analytics and visualization of data while manipulating it rapidly to derive business intelligence online, to run businesses smartly. 

For the Operational IT Management, the report delineates the operational issues businesses companies still encounter today in using legacy RDBMS systems despite their embracing fast access in-memory and solid state storage technologies. It details how IT is harnessing the emergent Big Data to manage massive amounts of data and new techniques such as parallelization and virtualization to solve complex problems in order to empower businesses with knowledgeable decision-making. 

Further, the report outlines how the market opportunities in Big Data are crystallizing and picking up serious steam and to understand the challenges still hindering widespread adoption and where potential users can expect the market to go. It presents a 5 year market forecast 2010-15, market shares of leaders, likely M& A scenarios and examines go-to-market plans of leaders to provide big data solutions for several vertical industries such as financial services, healthcare and media. Finally it provides recommendation for vendors, channel players, end users and investors for timely play to leverage the opportunities presented by the emerging big data markets.

Companies Analyzed

This service reviews the strategies, market positioning, and future direction of several providers in Big Data, including:
Alteryx, Attensity, Attivio, CloudIQ, Cloudera, Concurrent, Cray, DDN, Datameer, Dell, Digital Reasoning, EMC, GridGain, HP, HStreaming, Hadapt, Hortonworks, IBM, Informatica, Jaspersoft, KXEN, Karmasphere, Kitenga, MapR, Microsoft, Mu Sigma, NetApp, Objectivity, Opera Solutions, Oracle, ParAccel, Pentaho, Pervasive, Platfora, Progress Software, RainStor, Revolution Analytics, SAP, SAS, SGI, Splunk, SyncSort, TIBCO, Talend, Teradata, TideMark, Tresata, Versant, and Zettaset.

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Big Data & High-Performance-Analytics from SAS Deutschland

Monday, 29 April 2013

Experience capital gains using Low Latency Trading infrastructure


Capital Markets live and work on just one thing – Speed. Speed is seen in terms of speculation, execution and analysis. However, in the world of capital markets, the tick price of the financial instruments changes rapidly. In such scenarios, sometimes there is a delay between execution of orders and change of tick price. This delay is termed as Latency.

It’s high time since capital markets have shifted into the electronic domain and employ the use of computers and networks. Electronic Trading has taken place of traditional ring trading and is now governed by the control of exchanges. However, most of the times, the stock prices seem to change in a fraction of milliseconds and a time gap is introduced before the updated prices are visible to the users. To tackle this delay, exchanges and firms are constantly looking to implement a low latency trading infrastructure.

Latency introduced in the realm of capital markets is of two types. First type involves the delay between the actual price and price shown on the screen. The second type of delay involves the difference between the price at which order is placed and what it gets executed. Market participants and capital firms are constantly striving to reduce the latency in order to gain a competitive edge.

Many of the leading IT firms came up with a solution to inhibit or reduce latency significantly. They have introduced an infrastructure of systems, which comprises of high speed network connections and fast trading platforms. The collaboration of both these entities is believed to decrease the problem of latency. The infrastructure developed is called Low Latency Trading Infrastructure and is actively deployed on leading stock exchanges around the globe.

The first component comprises of network elements for connectivity. High speed routers and reliable internet connections are used. Further, to tackle issues of disconnection, leased lines are used. Internet Service Providers (ISP) provides data lines from servers having the least downtime. Thus, any change in the stock price will be immediately given to the trading software.

The second component consists of high speed trading platforms. The systems work on real time basis and instantly show responses displayed. They also facilitate instant order placements and the turnaround time taken to place an order from the user terminal to the exchange is dramatically reduced. Apart from this, all the tick prices are displayed on the client’s terminal on real time basis. So, instant change in price is quickly visible on the trader’s screen.

Low Latency trading platform has been implemented by New York Stock Exchange in association with popular investment and capital trading firms. It has immensely improved the scope of trading and helped individuals attain profitable returns.

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