Maxxphase introduces advanced data fabrics as a new and very innovative type of data architecture. We design and implement Advanced Data Fabrics using Compatible Data Modeling methods developed at Maxxphase. We compose our Advanced Data Fabrics from compatible datasets that are directly interoperable simply by design.
In contrast, a prior art Data Fabric is a monolithic dataset derived from integrating multiple disparate source datasets. However, ETL-based data integration is an older technology that is very tedious, complex, and expensive. ETL-based data integration is an outdated technology in a world of advanced data fabrics.
We form a Distributed Data Fabric from multiple compatible datasets instantiated from a single logical Compatible Data Model. Functionally, we enforce referential data integrity between the compatible datasets, which makes them directly interoperable. Each compatible dataset can exist in a different physical environment. For example, the compatible datasets can be on-premises, in a cloud, or managed by different database management systems. Directly interoperable datasets, instantiated from a single logical data model, spontaneously form a distributed data fabric.
Our Modular Data Fabrics are an enhancement of Distributed Data Fabrics. While we design the Distributed Data Fabric from a single logical data model, we design Modular Data Fabrics from multiple modularized logical data models. To modularize a logical data model, we encapsulate each logical data model within a ‘shell’ of Data Compatibility Standards (DCS) data entities.
When we instantiate a modular dataset from a modular data model, the shell of DCS data entities becomes a DCS Universal Dataset Gateway. The DCS Universal Dataset Gateway provides plug-and-play direct dataset interoperability between modular datasets. With the DCS Universal Dataset Gateway, we can dynamically combine modular datasets on-demand to form a Modular Data Fabric.
The Analytics-Ready Data Fabric is an enhanced type of Modular Data Fabric. The design of our DCS data entities is critical to the functionality of the instantiated modular datasets and the resulting modular data fabric. Our DCS data entities are required to deliver plug-and-play direct dataset interoperability. Beyond that, we design our DCS data entities to support data warehouse functionality, data chronology, data governance, standards version control, and audit trail. Every modular dataset inherits the dataset functionality supported by the DCS data entities and their instantiated DCS Universal Dataset Gateway.
In addition, you can denormalize your Compatible Modular Datasets to significantly improve performance from the normalized data form used for operational datasets. You can also store derived data aggregates in each compatible modular dataset. These derived data aggregates often enrich the modular dataset with summarized information that replaces commonly requested data. Any data functionality typically provided with analytic-ready datasets can be achieved for a modular analytics-ready dataset.
We designed the Golden Data Fabric as an advanced data fabric focusing on master data quality. Each DCS is associated with a single master data domain. Each master data domain of the fabric can have a Golden Modular Dataset designed explicitly as the trusted single source of truth for that master data domain. All compatible modular datasets of the Golden Data Fabric are directly interoperable with the Golden Modular Datasets. Therefore, all modular datasets have direct access to the same curated Golden Data Records.
Organizations struggle with their fragmented and disjointed disparate data architectures as these architectures rapidly approach their technical limits. Advanced data fabric architectures are seamless and have many advantages over these outdated, disparate data architectures. Advanced Data Fabrics are the only logical solution for organizations searching to evolve or are interested in more modern technologies, such as ML/AI.