A fully automated cloud database service optimized for analytic workloads, including data marts, data warehouses, and data lakes. It is preconfigured with columnar format, partitioning, and large joins to simplify and accelerate database provisioning, extracting, loading, and transforming data; running sophisticated reports; generating predictions; and creating machine learning models. With Autonomous Database, data scientists, business analysts, and non-experts can rapidly, easily, and cost-effectively discover business insights using data of any size and type. Built on Oracle Database and Oracle Exadata, Autonomous Database is available on Oracle Cloud Infrastructure for shared or dedicated deployments, and on-premises with Exadata Cloud@Customer and Dedicated Region Cloud@Customer.
Oracle Autonomous Database: Revolutionizing Data Management (0:30)
Watch Andrew Mendelsohn, EVP of Database Server Technologies at Oracle discuss how Oracle is automating data management for all users.
Detailed analysis by industry expert DSC illustrates why Oracle Autonomous Database for analytics and data warehousing is a better pick for the majority of global organizations.
Learn about applications and tools that are compatible with Autonomous Database for analytics and data warehousing.
Oracle Autonomous Database for analytics and data warehousing eliminates nearly all manual administrative tasks. It automates common tasks like backup, configuration, and patching. Uniquely, it continuously automates performance tuning and autoscaling, with no downtime, human intervention, or over-provisioning. This reduces administration effort by up to 90% and enables business teams to operate without help from IT.
The 2025 Imperative: Intelligent Automation NowAutonomous Database for analytics and data warehousing is the only complete solution that uses a converged database, providing built-in support for multimodel data and multiple workloads. It includes built-in self-service tools to improve the productivity of analysts, data scientists, and developers.
Autonomous for Business Success (PDF)Unlike other cloud data warehouse services, Autonomous Database for analytics and data warehousing offers three deployment choices.
Autonomous Database for analytics and data warehousing autonomously encrypts data at rest and in motion, protects regulated data, applies all security patches, and performs threat detection. In addition, customers can easily use Oracle Data Safe to conduct user and privilege analysis, sensitive data discovery, and protection, and activity auditing. Autonomous Database makes it easy to keep data safe from outsiders and insiders.
IDC Research: The Security Benefits of a Fully Managed Database Service (PDF)Autonomous management capabilities, such as provisioning, configuring, securing, tuning, and scaling, eliminate nearly all the manual and complex tasks that can introduce human error. Autonomous management will enable customers to run a high-performance, highly available, and secure data warehouse while reducing administrative costs.
FeaturesAutonomous Database for analytics and data warehousing 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.
FeaturesSecure Autonomous Database for analytics and data warehousing 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.
FeaturesData 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.
FeaturesBuild and deploy machine learning models in Oracle Autonomous Database for analytics and data warehousing 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.
FeaturesAutonomous Database includes graph database features to represent and manage complex data relationships. The graph analytics enable data scientists and developers to apply pattern recognition, classification, and statistical analysis for deeper context.
The spatial features in Autonomous Database for analytics and data warehousing address all forms of applications, spatial workloads, and datasets, including the most demanding, large-scale location intelligence and geospatial applications.
FeaturesIDC finds Autonomous Database customers obtain a 417% ROI in five years.
Learn moreWikibon finds Oracle Autonomous Database is 50% lower cost than AWS.
Learn more (PDF)KuppingerCole: Oracle Autonomous Database is ranked #1 overall for Cloud Database and Big Data security
Read the report (PDF)Learn how Oracle customers are using Oracle Autonomous Database to transform their businesses by redefining data warehousing through machine learning and automation
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Deploy a self-service departmental data warehouse to consolidate multiple enterprise systems, spreadsheets, and third-party data sources into a trusted, maintainable, and integrated dashboard. Integrated self-service data tools allow users to load and transform data with drag and drop, generate business models, quickly discover anomalies, and build machine learning (ML) models.
Answer more complex questions using all data. Simplify your enterprise data warehouse to support multimodal, converged data with autonomous capabilities.
Build and deploy machine learning models in Oracle Autonomous Database for analytics and data warehousing using scalable and optimized in-database algorithms.
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Common cloud industry practice is to define compute instances based on the number of virtual CPUs (vCPUs) they include. Each vCPU provides the capacity for one thread of execution. A vCPU does not provide a whole physical compute core, it’s part of a core. In contrast, Oracle’s x86 compute shapes use OCPUs which equate to physical CPU cores, each of which provides for two threads. To make it easier for customers to compare across cloud service providers, Oracle presents vCPU pricing on our web pages while billing is based on the number of OCPU time they consume. The per-hour OCPU rate customers are billed at is twice the vCPU price on the web pages since they receive two vCPUs of compute power instead of one.
Oracle data warehouses have been a mainstay in data architectures for decades. For good reason—data warehouses are scalable, highly secure, handle complex queries with high levels of concurrency, and are available to a wide range of tools and applications. With that said, data lakes have increasingly become a key part of data platforms.
Read the complete postProduct documentation and help center to deploy Autonomous Database for analytics and data warehousing for shared and dedicated infrastructure.
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