Oracle Analytics Cloud is a scalable and secure public cloud Single collaborative environment to manage technical, business, and OCI Data Catalog is a fully managed, self-service data discoveryĪnd governance solution for your enterprise data.
Joining it seamlessly with the most recent data by using hybrid tables in Object Storage can also be used as a cold storage layerįor the data warehouse by storing data that is used infrequently and then Without experiencing any degradation in performance or service reliability. Management interfaces let you easily start small and scale seamlessly, You can safely and securely store or retrieveĭata directly from the internet or from within the cloud platform. Storage can store an unlimited amount of unstructured data of any content Platform that offers reliable and cost-efficient data durability. OCI Object Storage is an internet-scale, high-performance storage Learning features and the related Notebooks interface. Score, and deploy machine learning models using in-database Oracle Machine This architecture contains the following OCI components:Ī fully managed Oracle and autoscaling autonomous database that (ADW), and rely on advanced security controls to greatly reduce risk. With the data lakehouse, you can leverage data from anywhere, normalizedĭata on the fly, run embedded AI/ML at Exadata scale, autoscale up/down at anytime Providing advanced reporting capabilities for internal and external usage.
Reliable and quick retrieval storage, being the source for machine learning modules, and This lakehouseĪrchitecture serves multiple purposes, including storing important data in secure Uses services such as Data Catalog and Oracle Analytics Cloud. Oracle Data Science, AI services, combining Autonomous Data Warehouse and data lakeĬapabilities in integration with other OCI services. The flexible lakehouse architecture supports multiple scenarios across based on Points among employees and the best next steps to take. Using sentiment analysis and entity recognition to identify the most common pain Recognition to identify key skills and education. Human resources: Automate resume screening by using entity.Use sentimentĪnalysis to identify urgent pain points and prioritize tickets. Customer support: Classify support tickets by product andĭepartment, so that tickets get to the appropriate team faster.See what they doĪnd don’t like, what new features they want, and how you compare to your Marketing: Analyze social media, reviews, and news to see whatĬustomers and industry experts are saying about your product.The service has multiple use cases, including: OCI Language is one of the most relevant AI services in this scenario, whichĬan help businesses improve their customer experience while reducing the time and effort Use case, AI-driven digital assistants are used, based on lakehouse data, for aĬonversational interface for apps and kiosks with actionable recommendations. Oracle Container Engine for Kubernetes is a robust platform that provides scalabilityĪnd additional control over microservices and applications.Īn example of a modern enterprise AI usage is the digital assistant. The different channels through which customers interact with the merchant,Īs visible in the diagram and mentioned above, often rely on tailor-made applications. The gold standard for low-code custom apps. Supported by ADW's OML notebooks (based on Apache Zeppelin) and accessible through OAC,īy using Data Science (JupyterLab/Python-centric), and Oracle APEX comes into picture as Spatial & Graph providing the necessary location support. With the OCI Object Storage, which here serves as a data lake, as an unlimited andĭata science and machine learning initiatives can result in outcomes suchĪs intelligent sale forecasts based on season, occurrence of marketing campaigns,Ĭharacteristics of the customer population (e.g. Machine learning on ADW brings the advantage of havingĪlgorithms right where the data is, for maximized performance. Loading, data transformations, business models, automatic insights, and built-inĬonverged database capabilities that enable simpler queries across multiple data typesĪnd machine learning analysis. Scaling, and backing up of the data warehouse. It automates provisioning, configuring, securing, tuning, The following diagram introduces the conceptual retail business lakehouseĭescription of the illustration retail-lakehouse-arch.pngĪutonomous Data Warehouse (ADW) is one of the central pieces of the OCIĭata lakehouse architecture. Lakehouse architecture can help accomplish. Such aĬhallenge calls for simplification and consolidation, which is something an OCI data Systems and data models and types, as well as an ever-growing quantity of data. One of the main complexities of the retail business is the multiplicity of
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