White Paper: Stone Bond Technologies
The Data Warehouse (DW) has been around some thirty years as essentially a repository forstoring corporate data. The effort to define, design, and implement new data sets in a data warehouse results in backlogs that are prohibitive to support the fast pace of today’s data needs.
Most companies will continue to use their Data Warehouse as they move to the more agile approach, but they will rely on it mostly as a historical data repository for reporting and analytics.
The Logical Data Warehouse (LDW) is a new data management architecture for analytics which combines the strengths of traditional repository warehouses with alternative data management and access strategy.
An overview on Data Virtualization for Logical Data Warehouse
Challenges with Classical Data Warehouse
A case for a new generation of data access (LDW)
Logical Data Warehouse for Business Intelligence
Leveraging Data Virtualization for next generation Data Warehouses
Data Virtualization for Logical Data Warehouse
By: NTP Software
File Tiering and Stubs – What You Exactly Need to Know To Make the Right Choice. We’ve all heard the saying, “The devil is in the details.” Nowhere is this truer than it is when talking about the stubs used in tiering. Stubs require the cooperation of the storage hosts, the network, protocols, security, end-user applications, and client systems. What works for one may or may not work for another. This informative whitepaper addresses all of the following issues: How do you stub a file? When the stub is gone, how does an end-user find their tiered files? Do you care whether all of your data reaches Tier 2, or is it all right for the tiering system to throw some of it away? If compliance becomes an issue, can your tiering solution do what will be required? Read this whitepaper to learn how the right stubbing mechanism can provide end-users and applications with a seamless and unchanged experience as your organization moves to realize the benefits and savings from tiering your files.
By: Service Objects
Collecting visitor and customer data through a variety of channels allows you to quickly grow your contact list. However, receiving high quality data isn’t necessarily a given. Whatever your touch points are with customers or prospects – including customer data, contest entry forms, phone surveys, lead generation and point of sale interactions – some data will be inaccurate, incomplete, or fraudulent. So how can your company avoid the challenges associated with bad data? Download this white paper and learn how to identify areas that could benefit from improved data quality, realign your data quality goals to improve your bottom line, optimize your human capital and even help the environment.
What is Data Management ?
Data management is the development and execution of policies and procedures in order to manage the information lifecycle needs of an enterprise ensuring the accessibility, reliability, and timeliness of the data for its users. Data Management enables organizations and enterprises to use data in: Organizing the enterprise data, Storing and preserving data for future re-use, Making data ready to use anytime, Share data with colleagues
What is virtualization ?
Virtualization, in computing, refers to creating a virtual vision rather than an actual vision of something, which includes computer network resources, virtual computer, storage devices, and hardware platform and so on. A technology in which an application, data storage, or operating system is abstracted away from the original underlying hardware or software is called virtualization. Virtualization uses a software layer namely hypervisor to emulate the hardware.
What is Data Warehouse ?
A data warehouse (DW) is a central repository or central database of numerous corporate information and data derived from operational systems and external data sources. In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for data analysis. It is considered as a core component of business intelligence.