Big Data for Finance

Q&A: GigaSpaces' Adi Paz on In-Memory Performance and Big Data

The In-Memory term is increasingly in use, and in the context of big data. We asked Adi Paz, vice president of marketing and business development at GigaSpaces Technologies, to explain the integration of in-memory and big data technologies, and what benefits can be expected.

Q: Let's begin with some GigaSpaces and XAP basics. Can you briefly describe what the company does, and what XAP is?

A: GigaSpaces is the pioneer of a new generation of in-memory computing platforms and a leading provider of big data solutions and cloud enabling technologies for mission-critical applications. The GigaSpaces solutions are designed from the ground up to run on any cloud environment, and offer a pain-free, evolutionary path to meet tomorrow’s IT challenges to enterprises in such industries as financial services, e-commerce, transportation/logistics, and telecom carriers.

Q&A: Panopticon's Willem De Geer on (Big) Data Visualisation in Capital Markets

When discussing big data, the point is often made that deriving value from the data, through analytics, is where the real business win is. Panopticon Software has been in the analysis business - specifically data visualisation - since before big data was a common term. So we found out more about the company, and how it is involved in the capital markets, from its managing director, Willem De Geer.

Q: How did Panopticon Software get its start, and what is its focus?

A: Development of Panopticon visual analysis tools began in 1999 at an emerging market brokerage, Brunswick Direct. The CTO, Sam Giertz, was facing a difficult challenge: how to efficiently deliver quality information on thousands of financial instruments in over 30 markets in a very short period of time. Giertz wanted his clients to be able to make sense of huge volumes of information quickly and then be able to make informed investment decisions based on that information.

Q&A: HPCC Systems' Flavio Villanustre on Not Map Reduce and Open Source

HPCC Systems - a unit of LexisNexis - has been quietly building out a big data processing platform for several years, using it internally to power several of its parent's services and applications. Now, it's open sourcing the not-map reduce platform, and selling a commercial version to third parties. We got the detail from Dr. Flavio Villanustre, vice president of infrastructure and products, at the company.

Q: How did HPCC Systems get formed, and what is its relationship to LexisNexis?

A: HPCC Systems is an initiative by LexisNexis Risk Solutions to release its core distributed data-intensive technology platform under an Apache open source license. This HPCC Systems platform was developed by LexisNexis Risk Solutions, a leader in providing information that helps customers across all industries and government predict, assess and manage risk. LexisNexis Risk Solutions is a $1.4 billion division of Reed Elsevier, which is a leading publisher and information provider serving customers in over 100 countries with more than 30,000 employees worldwide.

Q&A: Dell's Bob Barris on the ABCD of Big Data Analytics

Combining its server, storage and networking hardware with open source and partner software, and professional services, Dell is shaping up to be an IT heavyweight in the big data space.

We tapped Dell VP of sales for financial services Bob Barris to explain the company's offering, and he responded that it's as easy as ABC ... and D.

Q: Dell is transforming from a major hardware product company into one focused on delivering solutions.  How is it organised to address (a) the needs of financial markets participants (b) customers' big data initiatives, and (c) the convergence of the two?

A: Dell has a long history of activity in the financial markets going back to our very beginning in the 1980s.  Our hardware products, Dell servers, desktops and notebooks, have always had a prominent place on Wall Street.  However, more recently, Dell has transformed itself to an end-to-end technology provider, offering IT solutions designed to solve customer technology pain points based on various industry challenges.

Q&A: Thomson Reuters' Rich Brown on Machine Readable News, Social Media and Sentiment

BigDataForFinance.com's sister community - Low-Latency.com - just conducted an insightful Q&A with Rich Brown, who leads machine readable news programs at Thomson Reuters.  It's a topic that spans both low latency and big data, and you can read it here.

Q&A: Dataminr's Ted Bailey on Hotspots Among The Hashtags

Specialising in creating "actionable insights" for financial traders from the deafening noise that is Twitter, Dataminr recently made its own news by raising $13 million in Series B funding (for a total to date of $16 million).  We spoke to Dataminr founder and CEO Ted Bailey to find out more about the company and its big data challenge with the Twitter firehose. 

Q: How did Dataminr get started, and what is its focus?

A: I started the firm with two former roommates from Yale University – Jeff Kinsey and Sam Hendel – with combined experience in social media analytics,big data, and finance.  The company is focused on extracting value from social media for clients in finance and government.

Q&A: ParStream's Mike Hummel on Bringing Low Latency to Big Data

Bringing low latency to the world of big data is what ParStream - which recently raised $5.6 million in series A funding - has been working on now for several, years, with some impressive results.  We talked to the company's CEO Mike Hummel to find out more about the company and its technology.

Q: First, can you describe how ParStream got started, and what business problem are you looking to solve?

A: We founded ParStream in 2008 after we had identified a lack of database technology enabling real time big data applications for our customers.  Four years ago, our company got a contract to build a search engine for a travel package offering.  The application should be able to search through about seven billion data records against 20 parameters in less than 100 milliseconds.  We tried a lot of different database technologies, also NoSQL, but nothing worked.  This motivated us to develop our own technology.

Q&A: Benchmark Solutions' Tim Grant on OTC Market Modeling with Big Data

Pricing and transparency in the OTC markets has been a key regulatory focus since the credit crisis of 2008.  But unlike exchange-based markets, data from OTC trading is high in volume, comes in many different formats, and from many different sources - big data in realtime.  Tim Grant, head of sales and marketing at Benchmark Solutions, describes how his company makes sense of it all.

Q: When was Benchmark Solutions founded, and what does it do that's new, and needed?

A: Benchmark Solutions was founded in 2009 by Jim Toffey (formerly the CEO and founder of Tradeweb) and is financially backed by venerable private equity house, Warburg Pincus.  The company was established to solve the long-time pricing and transparency problem in the OTC fixed income markets, becoming the world’s first provider of real time pricing for US dollar corporate bonds and credit default swaps.

Q&A: SunGard's Neil Palmer on the Top Ten Trends

Sungard likes to publish top ten trends lists, and the latest focuses on big data in financial services.  We chatted to Neil Palmer, partner at SunGard’s consulting services’ advanced technology business, to find out some more behind the predictions.

Q: You just published SunGard's view of the ten big data trends for financial services in 2012.  What's the commonality in them?  What's the elevator pitch for big data trends over the next year?

A: Big data trends are being driven by a combination of an increased volume of data from internal and external system that needs to be rationalised and the drive to be more economical with existing hardware investments.  The elevator pitch we see from software and platform vendors is to be able ‘to achieve more with less’.

Q&A: RainStor's John Bantleman on Big Data Scale with Low Cost and Complexity

Big data storage and analytics is not just about MapReduce and Hadoop.  A number of specialised database products have grown up that look to solve the problems of data scale, while keeping a focus on costs, and overall complexity and manageability.  One such database is RainStor, from the company of the same name.  We spoke to RainStor's CEO John Bantleman about the database and how it's being deployed in the financial markets.

Q: First off, how did the company come to be, and what are the main business problems that RainStor is tackling?

A: RainStor was founded in 2004 with the purpose of designing a database from the ground-up to efficiently manage big data at the lowest cost with minimal complexity.  RainStor's patented database is append-only and is therefore ideal for environments that have machine-generated data with volume and velocity that needs to be collected and stored for ongoing query and analysis.