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Data lake architecture8/24/2023 The architecture has the following functional components:īatch ingest is useful for data that can't be ingested in real time or that is too costly to adapt for real-time ingestion. This abstraction facilitates agile approaches to development, migration to the target architecture, and the provision of a single reporting layer from multiple federated sources. For non-relational data, this layer contains one or more pools of data, either output from an analytical process or data optimized for a specific analytical task.Ībstracts the logical business view of the data for consumers. For relational technologies, data may be logically or physically structured in simple relational, longitudinal, dimensional or OLAP forms. Ingests and refines the data for use in each of the data layers in the architecture.įacilitates access and navigation of the data to show the current business view. The architecture focuses on the following logical divisions: The following diagram illustrates the functional architecture.ĭescription of the illustration lakehouse-functional.png Support for different architectural approaches, from a centralized lakehouse to a decentralized data mesh.Support for a diverse set of use cases including streaming, analytics, data science, and machine learning.Interoperability with any system or cloud that adheres to open standards.Modularity so that service use is use-case driven.Better cloud economics with autoscaling that adjusts cloud resources infrastructure to match the actual demand.Infrastructure (OCI) native services that are managed by Oracle and that reduce operational overhead The ability to leverage multiple compute engines, including open source engines, to process the same data for different use cases to achieve maximum data repurposing, liquidity, and usage.The ability to fully decouple storage and compute resources and to consume only the resources needed at any point in time.Governance and fine-grained data security that leverages a zero-trust security model.
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