The development of database technology is one of the defining achievements of the information technology era. It not only has been the key to dramatically improved record-keeping and business process automation, but also has enabled enterprises to get tactical and even strategic value out of the data that was stored.
Yet, that relational technology model has reached its limitations, as it was not built in anticipation of the big data movement which deals with a rapidly increasing volume and variety of data sources of all shapes and sizes, accompanied by soaring expectations for new insights and data-derived value.
A new semantic-based graph data model has emerged within the enterprise.
This data model has all of the advantages of the relational data model, but goes even further in providing for more intelligence built into the database itself, enabling greater elasticity to absorb the inevitable changes to data requirements, at cloud scales.
Relational technologies originated in the 1960s and 1970s, and were characterized by a tabular format in which data were stored in rows and columns. Empowered by Structured Query language (SQL), users were able to issue queries in adherence to data modeling configurations predicated on predefined schema. Significantly, the basics of this paradigm remained in place for the better part of 45 years and are still regularly deployed today.
Nevertheless, a number of gains in various facets of IT during the past two decades helped to propel operational database technology forward. Storage advancements made the amassing of greater amounts of data less expensive. In-memory capabilities surpassed traditional memory restrictions, and massively parallel distributed computing methods, in conjunction with greater CPU capabilities, highly accelerated traditional computing speeds. Consequently, the relational model began to expand.
That expansion has resulted in the emergence of semantic graph databases, which can do everything possible in relational systems—and so much more. Semantic graph databases have finally achieved performance parity with other databases, but now offer unprecedented flexibility and the ability to reasonably accommodate much richer varieties of data at volume.
The natural evolution of relational technology to semantic graph databases is underpinned by the numerous points of similarity between these two approaches. In each instance, the capabilities of graph exceed those of relational simply because database necessities are easier in a semantic graph environment. That ease of use, coupled with the commonalities between these two approaches, is directly responsible for the transition of operational database technology from relational to semantic graphs.
The smart graph database approach to data modeling typifies this fact. These databases utilize an expanding semantic model that readily incorporates new varieties of data sources and more easily adjusts to changed requirements as they arise. Conventional concerns about schema and structure no longer apply in this environment. Organizations merely take the data they already have and evolve a unified model based on standards to which additional sources and requirements must adhere. Subsequently, linking disparate data sets is far easier in a semantic graph setting.
The same principle applies to transformation and analytics. The semantic model includes reusable mapping from source systems to target ones for the purpose of transformation, including all relevant business, industry, and system information. That mapping also provides the basis for the generation of ETL jobs, without code, on the ETL tool of choice—including ones used with relational technologies. The unified model’s efficient linking of data provides a rich contextualization of relationships to inform analytics.
The intrinsic understanding of the relationships of the underlying data is the premier advantage of semantic graph technologies.
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