Oracle Autonomous Data Warehouse

Ensure consistent high performance

Autonomous Data Warehouse continuously monitors all aspects of system performance. It makes adjustments autonomously to ensure consistent high performance even as workloads, query types, and number of users vary.

Features

• Auto-scaling
• Auto-tuning
• Auto-indexing
• Hybrid Columnar Compression
• Columnar processing
• Smart Scan
• Automatic optimizer statistics gathering

Reduce risk with database security

Secure Autonomous Data Warehouse with a unified database security control center that identifies sensitive data and masks it, issues alerts on risky users and configurations, audits critical database activities, and discovers suspicious attempts to access data.

Features

• Transparent data encryption
• Encryption key management
• Privileged user and multifactor access control
• Data classification and discovery
• Database activity monitoring and blocking
• Consolidated auditing and reporting
• Data masking
• Data redaction

Self-service data management tools

Data tools provide a simple, self-service environment for loading data and making it available to their extended team for collaboration. Business and data analysts can easily load and transform data with drag-n-drop capabilities, generate business models, quickly discover anomalies, outliers and hidden patterns, understand data dependencies, and the impact of changes.

Features

• Data loading
• Data Transformation
• Business Model
• Data Insights
• Catalog

Machine learning for everyone

Build and deploy machine learning models in Oracle Autonomous Data Warehouse using scalable and optimized in-database algorithms. Oracle Machine Learning accelerates the creation of machine learning models for data scientists by eliminating the need to move data to dedicated machine learning systems.

Features

• In-database algorithms
• Automatic data preparation
• AutoML automates model development
• Easy model deployment through REST and SQL interfaces
• High performance, scalability and security
• Oracle Machine Learning Notebooks
• OML for Python
• OML Services
• OML AutoML UI
• Oracle Machine Learning for SQL

Uncover hidden relationships in data

Graph databases make it easier to manage, represent, and interact with complex relationships in data. The graph model enables data scientists and developers to apply pattern recognition, classification, and statistical analysis, all of which allow for more efficient analysis at scale against massive amounts of data.

Oracle Autonomous Database already has graph capabilities for analyzing and visualizing graph models, but will soon add Graph Studio for a complete, managed platform for graph analytics.

Graph Studio key features:

• Automated graph modeling
• Automated install, upgrade, and provisioning
• Autosave, backup, and checkpoint data restoration features
• Advanced notebooks and visualization
• Ability to schedule analysis
• Sample notebooks and workflows for different use cases

Making sense of location

The spatial features in Autonomous Data Warehouse address all forms of applications, spatial workloads, and datasets including the most demanding, large-scale location intelligence and geospatial applications.

Features

• Data model and comprehensive analytics for 2D spatial data
• High performance, parallel spatial processing
• Standards-based SQL and Java APIs
• Native JSON and REST support
• Location tracking server
• Contact tracing built-in functions
• Self-service Spatial Studio