Résultats Google Recherche d'images correspondant à. 4 Big Advantages of an Array Database for Big Data :: TabbFORUM - Where Capital Markets Speak. To address their big data problems, other industries have embraced Hadoop, which was built for managing big data and for distributing analytics to the data. Unfortunately for quantitative finance, Hadoop falls short because it is not very good at window-range selections. More important, getting value from big data is not about producing summary reports; it’s about using complex analytics (involving matrix-based linear algebra, for which Hadoop is not well suited) to find signals and recognize patterns. The common answer adopted by the finance industry long ago is to employ purpose-built databases for managing time-series tick data. But analytics on these solutions are limited to what you can run in memory. And the world’s ability to produce and analyze data is growing faster than Moore’s law increases computer memory.
[Related: “Big Data’s Real 'Three Vs': Veracity, Validity and Value”] So let’s start with the database part. So why does big data need an array database? 1. 2. 3. 4. Use Cases. The following use cases have been submitted from various sciences. They illustrate the kinds of analysis that various communities are doing or would like to do: New use cases will be added as we receive them and obtain permission from the authors to publish them here. The use cases are the key drivers of the SciDB design. If you are a scientist wanting to analyze your data in a particular way that is difficult with today's technology, or if you are responsible for designing, building or maintaining a data management system for a scientific project, please contact us, or submit a use case. A word to our non-scientific visitors dealing with large scale, complex analytics: although SciDB focuses on scientific analytics, challenging use cases from industrial users are welcome, too, as we believe there are many commonalities between scientific and industrial analytics.
Optimizing Algorithmic Trading Models Using an Array Database. A Quantitative Finance Use Case | Paradigm4, Inc. This post is part two of a two-part series on why you might care about an array database. Part one, “Why an Array Database?” Is recommended reading. Now, a word for you experts out there. We realize that quantitative analysis is complex; and we’ve simplified this use case for the sake of articulating an example to a larger audience. You’ll have to cut us some slack. A hedge fund wants to develop an algorithmic trading model that finds and exploits short-term market inefficiencies. Let’s set up a two-dimensional array; let one dimension be time and another be symbol. Did you see what happened when we set up the array?
With an array database, time and symbol are coordinate dimensions of the array; so it is very easy to select data along a single coordinate dimension or multiple dimensions at once. But our hedge fund wants to do something that will really burden traditional databases. What our hedge fund really wants to do is to compare the movement of every stock to every other stock.
Www.enterprisemanagement.com/about/about_pdfs/EMA_ResearchCalendar.pdf. Senior/Lead Hadoop Architect – Financial Services. Date: 24 Apr 2014 Location: United States, New York Salary: 200,000 - 220,000$ per Annum Senior/Lead Hadoop Architect – Financial Services Business Computer Software Company | Big Data New York City, NY Circa $220,000 - $220,000 total comp plus equity Key Skills: Apache Hadoop, PIG, HIVE, Flume, Sqoop, Java, Platform Architecture, SQL, MapReduce, Linux, Unix, Design Patterns, Hadoop Clusters, God written & verbal communication My client is a rapidly growing business computer software firm with a huge focus on excellence in Big Data and an existing global presence despite the firm still being in it’s infancy in terms of development. Key Responsibilities:- - Spearhead optimization and the group’s objectives for Architecting, designing and deploying Apache Hadoop environments - Work closely with customers and partners to establish these requirements and make significant changes - Assist in the design and build of reference configurations to enable customers and influence the product Apply Now.
Hadoop 2.0: The Capital Markets Dragon Slayer? Pivotal HD 2.0 to Help Enterprises To Get More Out of Hadoop With a Business Data Lake. Pivotal HD 2.0 will help companies get more out of their Hadoop investments by providing industrial-grade, enterprise capabilities and an upgraded Pivotal HD for gaining actionable insight in real-time. Pivotal HD 2.0 is the first platform to fully integrate an enterprise in-memory SQL datastore, GemFire XD, with advanced analytical data services on top of Hadoop to build out a flexible and comprehensive data science and big data toolset that is prepared for the enterprise.
To explain simply, this takes the capabilities of the all-encompassing data management Hadoop system and provides two key services bundled into Pivotal’s unique enterprise distribution: first, an in memory SQL Database that allows data to be ingested, processed, analyzed, and used immediately; and second, a powerful set of analytical services that provides businesses with a head start toward unlocking the value of their data. Enterprise-grade In-Memory Processing for Hadoop Analytics and Machine Learning Resources. Combating Financial Fraud with Big Data and Hadoop. While you race around checking off items from your holiday lists, banks are just as busy with their fraud prevention efforts.
According to a report by the Association of Certified Fraud Examiners, the typical organization loses 5% of its revenues to fraud each year, which translates to a projected annual fraud loss of over $3.5 trillion. Banks and other financial services companies are particularly vulnerable, due to the massive amount of financial data generated every day. Another challenge for this industry is the fact that the financial threat landscape has dramatically changed over the past few years, as sophisticated banking Trojans and new mobile threats pop up on a regular basis. The weeks prior to the Christmas holiday are typically a period of high malicious activity, and a new sophisticated banking Trojan is already gaining steam as the holidays approach. • Detect fraud more accurately.
. • Identify fraud sooner. Hadoop as a Data Management Hub :: TabbFORUM - Where Capital Markets Speak. In my last post (“Do Not Warehouse Your Data Warehouse Yet”),I suggested that the useful life of an EDW can be extended by offloading non-analytical functions onto Hadoop clusters. By doing this, Hadoop saves companies money even if it is used solely as an archive. Data that is infrequently accessed but must be retained for GRC (governance, regulatory, compliance) purposes can be moved from more expensive tiers of branded storage to clusters of commodity servers running Hadoop. This frees up the EDW to focus on high-performance processing and analytics on tier 1 data. In this scenario, Hadoop becomes another tier in a distributed data management architecture.
More data gives you more ways to search for and see patterns The bigger opportunity, however, is to perform analytics on a much larger data sets. Most business users think of their data in the context of time as opposed to quantity. Hadoop 2.0 opens the door to mainstream adoption Informed questions asked by informed users Conclusion.