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Oracle Autonomous Data Warehouse is a cloud data warehouse service that eliminates all the complexities of operating a data warehouse, securing data, and developing data-driven applications. It automates provisioning, configuring, securing, tuning, scaling, and backing up of the data warehouse. It includes tools for self-service data loading, data transformations, business models, automatic insights, and built-in converged database capabilities that enable simpler queries across multiple data types and machine learning analysis. It’s available in both the Oracle public cloud and customers' data centers with Oracle Cloud@Customer.
Watch EVP Andrew Mendelsohn, Oracle Database Server Technologies, to learn about Oracle's latest cloud data warehouse innovations.
Detailed analysis by industry expert DSC illustrates why Oracle Autonomous Data Warehouse is a better pick for the majority of global organizations
Learn about applications and tools that are compatible with Autonomous Data Warehouse.
Oracle Autonomous Data Warehouse eliminates nearly all manual administrative tasks. It automates common tasks like backup, configuration, and patching. Uniquely, it is also able to continuously automate 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.Unior Group, based in Zreče, Slovenia, manage 85 million records with no database administrators. (1:23)
Autonomous Data Warehouse 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.Analyst report: Oracle's Autonomous Data Warehouse Enhancements: Democratizing Simplicity (PDF)
Unlike other cloud data warehouse services, Autonomous Data Warehouse offers three deployment choices.
Autonomous Data Warehouse 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 Data Warehouse makes it easy to keep data safe from outsiders and insiders.DX Marketing, based in Savannah, Georgia, relies on Autonomous Data Warehouse to protect more than 260 million licensed records of US consumers and their associated private information. (1:54)
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.
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.
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.
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.
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.
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.
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.
Learn how Oracle customers are using Oracle Autonomous Database to transform their businesses by redefining data warehousing through machine learning and automation
Director, Cloud Data Warehouse in the Finance Industry 1
Deploy a self-service departmental data warehouse to consolidate data from multiple spreadsheets and other flat-file data sources into a trusted, maintainable, and high-quality dashboards.
Consolidate data from multiple enterprise systems, spreadsheets, and 3rd party data sources into an integrated and analytical data store.
Simplify your enterprise data warehouse to support multimodel, converged data with autonomous capabilities.
Combine the abilities of a data lake and a data warehouse to manage any data type for business analysis and machine learning.
After the introduction of Autonomous Data Warehouse, organizations of all sizes recognized how simple it could be to provision a data warehouse. Since Autonomous Data Warehouse requires no operational administration (and thus does not require a database administrator), a cloud data warehouse is within the reach of many more organizations than before.Read the complete post
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