Research: Top 10 Data Virtualization Patterns

Composite Software Inc. has defined 'Top 10' data virtualization patterns in a new report for enterprise architects and integration teams.

Tags: Data Virtualization, Integration, Pattern, Ferguson, Business, Report, Data Warehousing,


Composite Software Inc. has defined 'Top 10' data virtualization patterns in a new report for enterprise architects and integration teams.

The report, "Maximizing the Business Value of Data Virtualization," uses descriptions and diagrams to help IT professionals evaluate and implement the most popular data virtualization use patterns for their enterprise.

The paper is authored by Mike Ferguson, a leading independent Business Intelligence (BI)/Data Integration (DI) analyst based in the UK, The work was sponsored by Composite Software. Data virtualization is the "on-demand real time integration of data that resides in multiple underlying data sources," Ferguson said.

The paper, which also is aimed at data architects, data warehouse teams, and BI teams, notes common use cases for each pattern, including the reasons and rationale for selecting each pattern. "Data Virtualization software offers a lot of flexibility," said Ferguson, in a statement. "My paper documents the most popular uses of this technology to help developers and business users exploit that flexibility and get the most out of their investment in data virtualization."

The Top 10 Data Virtualization Patterns covered in the report:

  • BI/Performance Management Integration
  • Data Discovery
  • Holistic Data View
  • Virtual Data Mart
  • Virtual Data Source
  • Virtual MDM Pattern
  • On-Demand Information Services
  • Virtual ODS pattern
  • Heterogeneous Replication, and
  • Logical Archive
  • .

    According to the paper, these tools first appeared in the market under the banner of extract, transform and load (ETL) tools targeted toward to consolidating data in the data warehousing market.

    "Since then we have seen the need for data integration stretch way beyond data warehousing into all kinds of uses such as data migration, data synchronization and creation of master data management (MDM) hubs," Ferguson said in the paper. "However, data integration does not always have to be about data consolidation. It also includes data virtualization (also known as data federation) which addresses the increasing need for on-demand data integration."

    The white paper can be downloaded here.



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