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RBC Capital Markets, LLC ("RBCCM LLC" or the “Firm”) is committed to protecting its employees, clients and their assets at all times, including during emergencies or significant business disruptions. The Firm’s Enterprise Business Continuity Program has been developed to provide a reasonable, but not absolute, assurance of business continuity in the event of a disruption to the Firm’s normal operations.
Bewerbung 2010 Hotel & Spa Concepts 1. Ihr Konzept entspricht den genannten Kriterien und steht – ganz im Sinne der Spa-Philosophie – für den Einklang von Körper, Geist und Seele. 2. I hre Bewerbung für die Hotel- und Spa-Kategorien er folgt ausschließlich über die neuen Online Formulare. Unter www.gala.de/spa-awards können Sie die hinterlegten Fragebögen einfach ausfüllen sowie Bilder bis zu 10 MB hochladen. 3. Kontakt für Rückfragen: Ariane Gerber, Redaktion GALA Schaarsteinweg 14, D-20459 Hamburg Telefon: +49.40.3703-4387 Fax: +49.40.3703-5883 email@example.com 4. Der Link für Ihre Online-Bewerbung: www.gala.de/spa-awards GALA, Gruner + Jahr AG & Co KG Am Baumwall 11, D-20459 Hamburg Telefon +49.40.3703-4387, www.gala.de/spa-awards Luxury Hotel City/Resort Neu: nur noch online Hotel & Spa Concepts Die Bewerbung Neu: nur noch onli ne ! 2010 wird unsere Jury zum 14. Mal die besten Pflegepro ukte, Treatments, Luxury Hotels und Innovative Spa d Concepts aus 24 Ländern weltweit prämieren. Innovative Spa Concepts Prämiert wird in dieser Kategorie ein 5-Sterne-Hotel, das über einen exklusiven Spa-Bereich ab 1.000 m2 verfügt, welcher die ganzheitlichen Bedürfnisse von aktiver und passiver Ent spannung in Angebot, Design und Service kompetent mit einander vereint. Trends setzen und neue Wege aufweisen für ein noch schöneres Spa-Erlebnis: Das ist das wichtigste Kriterium für Bewerber dieser Kategorie. Die Chance für eine Be werbung gilt hier für Hotels mit einem absolut innovativen Spa-Konzept ebenso wie für ein Day-Spa oder SpecialInterest-Spa. Hotel: Das 5-Sterne-Haus überzeugt auch im Spa-Bereich durch konsequent stilvolles Ambiente. Die großzügige Ver wöhnatmosphäre und der exzellente Service begeistern. Die absolute Authentizität der Werbeversprechen ist ein „Must“. Zimmerausstattung: Ein luxuriöses Ambiente ist ebenso selbstverständlich wie Blumen, frisches Obst, Mineralwasser, Pflegeset, Bademantel und -schuhe.
Please check the part-number(s) for your application against the part-number(s) listed on the instruction sheet. DO NOT USE ANY WASHERS with ARP Flywheel Bolts. They are designed to be installed without them. Note: ARP will NOT be responsible for any failures resulting from using a washer with this kit. Make sure there is an adequate chamfer around the bolt holes on the flywheel to clear the radius under the head of the bolt. Lubricate the threads of the bolt with LOCTITE 242 and the under head of the bolt with ARP ULTRATORQUE FASTENER ASSEMBLY LUBRICANT. Then install the flywheel onto the crankshaft and tighten the bolts hand tight. Using an alternating or criss cross pattern, torque the bolts to 95 ft lbs using the specified lubricants in Step 4. If you have any questions or need additional information please contact us at (805) 339-2200 or by FAX at (805) 650-0742 Flywheel Bolt without Washer- Installation
In this paper, we examine a number of SQL and socalled “NoSQL” data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability. Note: Bibliographic references for systems are not listed, but URLs for more information can be found in the System References table at the end of this paper. Caveat: Statements in this paper are based on sources and documentation that may not be reliable, and the systems described are “moving targets,” so some statements may be incorrect. Verify through other sources before depending on information here. Nevertheless, we hope this comprehensive survey is useful! Check for future corrections on the author’s web site cattell.net/datastores. Disclosure: The author is on the technical advisory board of Schooner Technologies and has a consulting business advising on scalable databases.
As companies deal with ever larger amounts of data and increasingly demanding workloads, a new class of databases has taken hold. Dubbed “NoSQL”, these databases trade some of the features used by traditional relational databases in exchange for increased performance and/or partition tolerance. But as NoSQL solutions have proliferated and differentiated themselves (into key-value stores, document databases, graph databases, and “NewSQL”), trying to evaluate the database landscape for a particular class of problem becomes more and more difficult. In this paper we attempt to answer this question for one specific, but critical, class of functionality – applications that need the highest possible raw performance for a reliable storage engine. There have been a few attempts to provide standardized tools to measure performance or other characteristics, but these have been hobbled by the lack of a clear mandate on exactly what they’re testing, plus an inability to measure load at the highest volumes. In addition, there is an implicit tradeoff between the consistency and durability requirements of an application and the maximum throughput that can be processed. What is needed is not an attempt to quantify every NoSQL solution into one artificial bucket, but a more systemic analysis of how some of these databases can achieve under assumptions that mirror real-world application needs. We attempted to provide a comprehensive answer to one specific set of use cases for NoSQL databases -- consumer-facing applications which require extremely high throughput and low latency, and whose information can be represented using a key-value schema. In particular, we look at two common scenarios.
CUSTOMER TESTIMONIAL: FRONT PORCH DIGITAL’S RAPID APPLICATION DEVELOPMENT Front Porch Digital, Inc. is a world leader in digital asset workflow management serving global leaders in the entertainment industry. Front Porch Digital recently integrated Stretchr into its application development processes. “Stretchr has fundamentally changed the way we approach data systems development. Today’s data comes in so many shapes and sizes and is always changing, requiring you to spend a huge amount of time designing and editing schemas in traditional databases or developing expertise in NoSQL technology. With Stretchr all of that time and complexity goes away. You simply acquire the data, from any source and in any form. Stretchr then organizes the data for you based on how your users consume it – it couldn’t be simpler. Our first integration with Stretchr took an afternoon, and was effectively the insertion of one line of code into our existing application. So happy are we with the way Stretchr works and performs that we are tightly integrating our newest products with Stretchr, cutting development times significantly”.
In this paper, I describe some of the recent developments in the database management area, in particular the NoSQL phenomenon and the hoopla associated with it. The goal of the paper is not to do an exhaustive survey of NoSQL systems. The aim is to do a broad brush analysis of what these developments mean - the good and the bad aspects! Based on my more than three decades of database systems work in the research and product arenas, I will outline what are many of the pitfalls to avoid since there is currently a mad rush to develop and adopt a plethora of NoSQL systems in a segment of the IT population, including the research community. In rushing to develop these systems to overcome some of the shortcomings of the relational systems, many good principles of the latter, which go beyond the relational model and the SQL language, have been left by the wayside. Now many of the features that were initially discarded as unnecessary in the NoSQL systems are being brought in, but unfortunately in ad hoc ways. Hopefully, the lessons learnt over three decades with relational and other systems would not go to waste and we wouldn’t let history repeat itself with respect to simple minded approaches leading to enormous pain later on for developers as well as users of the NoSQL systems! Caveat: What I express in this paper are my personal opinions and they do not necessarily reflect the opinions of my employer.
Many organizations today would seemingly be content with having achieved an information architecture that features a broad-scope enterprise resource management environment feeding data in batch for reporting and analytics to a robust data warehouse environment. As a bonus, the data warehouses in this post-operational environment may consist of solid-state components and automated archival abilities. Irreversibly, the environment also has been inundated with data marts fed from original source and from the data warehouse itself. There is frequently a multidimensional database in the mix. Or a hundred. If there is any contentment with such an architecture, it will be short-lived. With information the “new gold” for companies, each shop must do everything it can to nurture, protect, make available and otherwise exploit the information asset. This will frequently mean venturing into new technology domains for the management of the asset. One may be tempted to consider the NoSQL movement as the epitome of these new technology domains. However, many possibilities have been laid on the table by the vendor community in the years prior to NoSQL. Most have merit in an enterprise today. We clearly need to get away from the winner-take-all mentality where every workload – sometimes whether it is analytical or operational – will be solved the same way as the last one. Frequently, that way was with a data integration operation with the data warehouse followed by the deployment of more reports in the business intelligence tool. Force-fitting a workload into a technology that it was not designed for creates more problems than it solves.
that is deduced directly from the structure of the program even in the absence of any explicit type declaration or annotation. We present a calculus for processing semistructured data that spans differences of application area among several novel query languages, broadly categorized as “NoSQL”. This calculus lets users deﬁne their own operators, capturing a wider range of data processing capabilities, whilst providing a typing precision so far typical only of primitive hard-coded operators. The type inference algorithm is based on semantic type checking, resulting in type information that is both precise, and ﬂexible enough to handle structured and semistructured data. We illustrate the use of this calculus by encoding a large fragment of Jaql, including operations and iterators over JSON, embedded SQL expressions, and co-grouping, and show how the encoding directly yields a typing discipline for Jaql as it is, namely without the addition of any type deﬁnition or type annotation in the code.