March 12, 2013
SAN MATEO, Calif., March 12 — SnapLogic, the leader in enterprise cloud integration, today announced new enterprise platform enhancements that enable users to more quickly build, deploy and efficiently manage multiple high-volume, data-intensive integration projects. This latest release of SnapLogic helps enterprises improve data provisioning among SaaS and on-premises apps while also promoting greater agility by enabling private enterprise SnapStores.
"Enterprises need to manage significantly larger volumes of data than they have in the past, and they need faster, easier ways of doing this. Our customers have increased the velocity of their integrations by a factor of 10, scaling their usage of SnapLogic in their organizations," said Zeb Mahmood, SnapLogic's principal of product and strategy. "Our latest release delivers several new designer and server enhancements, which further reduce the time needed to build, maintain and monitor integrations across the enterprise."
New productivity-enhancing updates to the SnapLogic Integration Solution include:
Greater Control and Agility: Building on the success of SnapLogic's open SnapStore marketplace, which contains a library of 150 intelligent connectors called Snaps, enterprise IT departments and SnapLogic's implementation partners can now easily maintain their own Snap repositories. These private SnapStores are seamlessly integrated with the SnapLogic Designer, so central IT users can easily download, install, and upgrade the Snaps they wish to include, and offer self-service access to business units integrating their own applications. With the private SnapStores, internal users can select custom or pre-built connectors specific to their group's application integration needs.
Faster Time-to-Integration: The SnapLogic Designer now features new time-saving capabilities for integration developers, such as viewing server-side logs, previewing large unstructured data records, and browsing the server's file system. In addition, this release includes several new pre-built integration components and increased support for various data formats, such as large database objects.
Improved Visibility into Integrations: SnapLogic's management console now makes it dramatically easier for administrators to visually monitor and manage integrations throughout the enterprise. For example, admins can view a summary or detailed history of individual integrations, see visual indicators for network connectivity status, and monitor real-time health metrics of all integration points.
Supports Unstructured Streaming and Data Management: For enterprises integrating data from the cloud to on-premises systems, database components can now return Character Large Object (CLOB) and Binary Large Object (BLOB) database types as strings, allowing easier integrations for multiple streaming data projects. For added speed of integrations, the DB Wizard recommends appropriate field mappings for added simplicity. Optimized in-memory processing of streaming data is also part of the functionality that increases productivity.
Increased Peace of Mind: With improved support for token-based authentications, encrypted login credentials, single-sign-ons and protocols such as secure FTP, SnapLogic's latest platform release gives users confidence that their data is managed securely, whether in the cloud or on-premise.
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
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The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
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When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
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