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Dataarchitecture definition Dataarchitecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations dataarchitecture is the purview of data architects.
Data Gets Meshier. 2022 will bring further momentum behind modular enterprise architectures like data mesh. The data mesh addresses the problems characteristic of large, complex, monolithic dataarchitectures by dividing the system into discrete domains managed by smaller, cross-functional teams.
While many organizations still struggle to get started, the most innovative organizations are using modern analytics to improve business outcomes, deliver personalized experiences, monetize data as an asset, and prepare for the unexpected. Being locked into a dataarchitecture that can’t evolve isn’t acceptable.”
Since then, customer demands for better scale, higher throughput, and agility in handling a wide variety of changing, but increasingly business critical analytics and machinelearning use cases has exploded, and we have been keeping pace. At AWS re:Invent, we announced support for LLMs as preview.
These lakes power mission critical large scale data analytics, business intelligence (BI), and machinelearning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.
While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.
These lakes power mission critical large scale data analytics, business intelligence (BI), and machinelearning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.
In a nod to AC/DC, a wink to Gartner’s research report, Data Catalogs Are the New Black in Data Management and Analytics , and inspiration from the inaugural Forrester Wave : MachineLearningData Catalogs , we have temporarily set aside our Alation orange and have been rocking “black” for the Alation MLDC World Tour.
To drive the vision of becoming a data-enabled organisation, UOB developed the EDAG (Enterprise DataArchitecture and Governance) platform. The platform is built on a data lake that centralises data in UOB business units across the organisation.
A data fabric utilizes an integrated data layer over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of data across enterprises, including hybrid and multi-cloud platforms.
Foundation models (FMs) are large machinelearning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. Streaming data facilitates the constant flow of diverse and up-to-date information, enhancing the models’ ability to adapt and generate more accurate, contextually relevant outputs.
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