Modern Data Warehouse provides an integrated machine-learning (ML) solution that enables customer insights and business intelligence to make faster business decisions. Its easy to get started with self-service capabilities, and Oracle's data warehouse automation eliminates management complexity to simplify analysis.
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Complete, integrated solution of data warehouse, integration, data lake, data science, and analytics services. Customers can load any data, store in a data warehouse or data lakes, and transform, catalog, govern, visualize, analyze, and build ML models. With our integrated solution, customers are able to leverage security policies across the modern data warehouse with an integrated data lake, as well as query the data lake from the data warehouse. A single solution with built-in support for multimodel data and multiple workloads such as analytical SQL, in-database machine learning, graph, and spatial, eliminates the development and deployment complexity.Data Warehouse use case patterns
Oracle provides web-based user interfaces for self-service provisioning, data loading, data transformations, and data analysis. It takes only a few minutes to provision and start analyzing data. Existing Oracle Database customers can drag-and-drop data connectors and operators, data models, and third-party tools, making it simple to modernize.Take the Autonomous Data Warehouse tour
Oracle provides built-in data management and analytics tools like Oracle Spatial and Graph with Oracle Autonomous Data Warehouse, easy integration with Oracle Analytics Cloud, support for other popular BI tools, and services to build and deploy machine-learning models. This comprehensive set of tools and services enables customers to create agile organizations that move faster.Read the ebook (PDF)
Autonomous management enables customers to run a high-performance, highly available, and secure data warehouses while eliminating administrative complexity and reducing costs. Autonomous Data Warehouse automates provisioning, configuring, securing, tuning, scaling, backing-up, and repairing data warehouses. Autonomous Data Warehouse is the only solution that auto-scales elastically and provides complete data security. Other vendors lack fine-grained access controls, sensitive data controls and risk assessments, database firewalls, and more.Autonomous for Business Success (PDF)
Analysts need an efficient way to consolidate data from multiple spreadsheets and flat-file data sources into a trusted, maintainable, and query-optimized data store. Load and optimize data from multiple sources into a departmental data warehouse so business users can analyze the data and gain actionable insights.
Analysts need an efficient way to consolidate data from multiple applications, spreadsheets, and other data sources into a trusted, maintainable, and query-optimized data store. Load and analyze data from Oracle E-Business Suite and other sources using self-service data tools so departments can gain actionable insights.
Human resource data is often distributed in multiple systems across the enterprise and can't easily be integrated and analyzed to produce actionable insights. Enrich enterprise application data from PeopleSoft Human Capital Management with raw data and event data to produce predictive insights through an enterprise data warehouse.
Combine the abilities of a data lake and a data warehouse to process streaming data and a broad range of enterprise data resources and leverage the data for business analysis and machine learning.
Process streaming event and log data for predictive maintenance. Apply advanced analytics and data science capabilities to understand the context for an actionable event, gain insight, and create a response.
With graph analytics, data scientists and developers can manage, represent, and interact with complex relationships in data to perform fraud detection in banking, enable deeper customer 360-degree analysis in marketing, and create more discoveries in the pharmaceutical world.
Oracle Autonomous Database already has graph capabilities for analyzing and visualizing graph models, which avoid excessive data integration and movement, but will soon add Graph Studio to automate graph data management and simplify modeling, analysis, and visualization.
Data-driven applications (apps) operate on a diverse set of data (spatial, documents, sensor, transactional) pulled from multiple different sources, often in realtime. To help businesses make faster, more informed decisions, these applications are required to create value from data in very different ways to traditional applications.Read the complete post
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