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Modern Data Warehouse provides an integrated machine learning solution that enables customers insights and business intelligence to make business decisions faster. It’s easy to get started, and Oracle's data warehouse automation eliminates management complexity to simplify analysis.
Learn about Modern Data Warehouses from Andy Mendelsohn and Oracle customers Outfront Media, Adventist Health, and CERN.
Learn how to create a modern data warehouse, set up a data lake, or experiment with machine learning using a hands-on workshop with step-by-step instructions.
Complete, integrated solution of data warehouse, integration, ETL, data lake, data science, and analytics services. Customers can ingest any data batch, streaming or realtime, store in data warehouse or data lakes, catalog and govern, visualize and analyze, and build and deploy machine learning solutions. With our integrated solution, customer are able to leverage security policies across the data warehouse and data lake, as well as query the data lake from the data warehouse. Built-in support for multimodel data and multiple workloads such as analytical SQL, machine learning, graph, and spatial in a single database instance eliminates the integration complexity and administration that is required with other providers.Data Warehouse use case patterns
Oracle provides web-based user interfaces for self-service provisioning, data loading, and data analysis. It takes only a few minutes to provision and start analyzing data, no integrations are required. Existing Oracle Database customers are able to maintain the same data models, tools, and ETLs, making it simple to modernize.Take the Autonomous Data Warehouse tour
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.Read the ebook (PDF)
Autonomous Data Warehouse provides automated tuning and runs on Exadata. Oracle is the only vendor with automated turning, which automatically creates/drops data summaries for analytics, creates/drops columnar vector-processing formats, and automatically parallelizes workload to keep queries running fast. The Exadata-engineered system is not the commodity hardware other vendors use. It features the fastest NVMe storage, the fastest RDMA network, and advanced caching algorithms.Customer experiences with cloud data warehousing platforms (PDF)
Oracle provides built-in analytics tools with 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.Autonomous for Business Success (PDF)
Analysts need an efficient way to consolidate data from multiple spreadsheets and other flat-file data sources into a trusted, maintainable, and query-optimized source. Load and optimize data from multiple sources into a centralized data warehouse so departments can analyze the data and gain actionable insights.
Analysts need an efficient way to consolidate data from multiple financial systems, spreadsheets, and other data sources into a trusted, maintainable, and query-optimized source. Load and optimize data from Oracle E-Business Suite and other sources into a centralized data warehouse location for analysis 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.
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.
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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