The Compatible Data Fabric
The Compatible Data Architecture can best be described as a compatible, modular, analytics-ready data fabric. This Compatible Data Fabric is formed spontaneously from a group of multi-faceted compatible datasets. Each facet of each compatible dataset is a potential join with every other compatible dataset. The Compatible Data Fabric contains many threads that increase data access flexibility.
Disparate data architectures, commonly used today, are composed of incompatible datasets connected by data transformation software that moves and transforms data from one incompatible dataset to another. This data transformation software has been characterized as the data integration hairball because of its complexity. In contrast, the Compatible Data Architecture relies on dynamic on-demand dataset blending using the direct dataset interoperability of the compatible modular datasets. The Compatible Data Architecture is clearly a more innovative, more flexible, and less complex data architecture.
The Compatible Data Architecture composed of Compatible Datasets
Enriching Your Data Architecture
Amazingly, Compatible Data Architectures are compatible with and noninvasive to existing data architectures. Just as the Data Compatibility Standards enrich existing datasets without modifying structural metadata, data content, or interfering with their related software, enriching your data architecture is also noninvasive. All existing data architecture components continue to function unincumbered by the dataset commonality which adds dynamic dataset interoperability to each of your data architecture components.
Information Building
Data Compatibility is conducive to information building. Since each compatible dataset supports data warehouse functionality, derived, aggregated, and historical data may enrich each modular plug-and-play dataset. In addition, direct dataset interoperability may be used to blend multiple compatible datasets to materialize a new, consolidated, compatible dataset for repeated detailed data analysis. This form of information building is extremely efficient compared to traditional data transformation and incompatible dataset consolidation methods.
The Business Perspective
Never before has a business had such informational power at their fingertips. The Compatible Data Fabric provides a single source of consistent and trusted instantly available data. Within the data fabric, each compatible dataset forms direct links with every other compatible dataset. These dataset links are the weave of your Compatible Data Fabric. All your data fits together like the pieces of a jigsaw puzzle, providing a 360ᴼ view of all your data.
The Compatible Data Fabric also supports all the functionality and power of a data warehouse. This embedded data warehouse functionality is an enormous advantage over the conventional data fabric that is merely a data source for constructing a data warehouse. Business intelligence and analytics are intrinsic to your Compatible Data Fabric.
The Technical Perspective
Maxxphase implements compatible datasets in a multi-dimensional form, where each Data Compatibility Standard supports a standardized hierarchy of its master data domain. Since compatible datasets are data integrated without data transformations, they are also directly interoperable. Each level of granularity for every Compatible Data Standard promotes direct data interoperability across compatible datasets. Each compatible dataset becomes an integral component in a multi-tiered Compatible Data Fabric. The data weaves between compatible datasets are very comprehensive. As such, the Compatible Data Fabric formed from these datasets also directly supports multi-dimensional analytics.
Each compatible dataset incorporates all data warehouse functionality, such as roll-ups and drill-downs, slicing, and dicing. You can dynamically blend various combinations of compatible datasets, forming a virtualized or a materialized business intelligence environment specific to an analytic need.
Traditional data fabrics tend to have several performance issues. In contrast, compatible datasets are very agile and support data retrieval performance enhancements. The direct data weaves of the Compatible Data Fabric are far more efficient than the indirect, transformed data weaves used in traditional data fabrics. Compatible datasets also support the materialization of aggregate data and consolidated data to eliminate inefficient data retrieval. Compatible Data Fabrics will easily outperform traditional data fabrics.
Compatible datasets fit together with data blending because the data content in compatible datasets are integrated and there is extensive direct dataset interoperability. The resulting Compatible Data Fabric has all the functionality of a traditional data fabric and a whole lot more. If you have an interest in data fabrics, you need to find out about Compatible Data Fabrics.