data warehouse

Such data is often stored in data warehouses and data marts specifically intended for management decision support.

The centralized approach is typically used for production applications, data warehouses and operational data stores.

Fact (or history) tables in data warehouses make excellent candidates for use with each partitioning feature, as you see in Table 4 below.

Oddly enough, even the largest game worlds are miniscule compared to data warehouses employed by thousands of companies around the world.

The data in data warehouses and data marts is usually updated frequently, and the data loads are typically very large.

This Appliance arrives tuned and configured out of the box, providing extreme analytics for overburdened enterprise data warehouses.

As you are building your data warehouse, think about which dimensions can be reused in other data warehouses.

Pre-aggregation is one of the most effective techniques in OLAP to ensure quick responses to user queries in data warehouses.

Unlike on-line transaction processing databases, data warehouses do not contain the most current information.

In fact, Oracle sold more data warehouses on top of IBM server and storage technology than any other hardware.

Range-based partitioning, which helps companies roll in and roll out data over time (a common requirement in data warehouses).

This pattern is different from standard information integration patterns used to build data warehouses or data marts.

Data warehouses and data marts are very similar technologies, say experts, but they usually service different types of clients.

Because databases take more time on tuning table joins than a single table, data warehouses usually don't work well with it.

Our products include business intelligence tools, data warehouses and data marts, and corporate performance management applications.

Moreover, data warehouses can store and integrate historical information and support complex multidimensional queries.

Data warehouses usually store many months or years of data. This is to support historical analysis.

Administrators, application writers, and analysts operating data warehouses can benefit from task automation.

Most organizations today have multiple data warehouses acquired through internal development or mergers or acquisitions.

Traditional data warehouses and OLAP applications emphasize particularly on the historic data analysis.

In data warehouses, the size of fact tables or history tables poses challenges to designers and administrators.

Essentially, MDC is a feature that maximizes performance of queries, especially those commonly found in data warehouses.

Consequently, data architects are evaluating options for integrating XML into their data warehouses.

So, data architects are increasingly evaluating options for integrating XML into their data warehouses.

The source data for OLAP is commonly stored in data warehouses in a relational database.

XQuery bridges these data warehouses by showing which products cause the most unresolved support calls.

XQuery offers a solution to assist OLAP by providing a query-based link between multiple data warehouses.

Files are a common source of operational data that businesses need to integrate into their data warehouses.

It provides a single and complete view of customers, partners, products, and business through data warehouses or federation.

A data mart is a giant "superstore" of data warehouses from several companies.

Large scale data warehouses are considered in addition to single service data marts, and the unique data requirements are mapped out.

File systems, database records, or systems such as data warehouses, provided that the grid application gains the necessary access rights.

I have been building data warehouses using Informix for over 12 years, as it is lighting-fast for loads and queries.

I have been building data warehouses with Informix for more than 18 years, and it has been getting better and faster with each new release.

In data warehouses, materialized views are used to compute and store aggregated data such as sums and averages.

Based on the above, we view data warehousing as the process of constructing and using data warehouses.

And, we build enterprise data warehouses and transactional master data hubs to store the end results.

For example, most data warehouses have a time dimension, but this almost always must be created independently.

Just four years ago, WinterCorp identified the world's largest databases, which were data warehouses measuring over 100 TB.

Data warehouses will continue to play a big role in many enterprises, says IBM's Deutsch.

Performance across the wide range of workload characteristics found in data warehouses

An Improved Dimension Hierarchy Aggregate Cube Storage Structure for Data Warehouses

Information architecture and data model for developing data warehouses

Distinctive design challenges with data warehouses

Analysis of Security and Countermeasures for Data Warehouses

Half Automatization of the Architecture of Large Data Warehouses

The Problems and Methods of Quality Management in Data Warehouses

Maintaining Temporal Views in Data Warehouses

On the Application of Data Warehouses in Chinese Enterprises

Policy of optimizing storage and indexing in data warehouses

Research on issues in developing large data warehouses

A novel method to support range queries on parallel data warehouses

Research into data modeling technology of data warehouses for electric power corporations based on IEC

On the design of enterprise-wide data warehouses and the critical factors in the development