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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.
By far the best tier-1 cloud database platform.
Learn about applications and tools that are compatible with Autonomous Data Warehouse.
Oracle Autonomous Data Warehouse eliminates nearly all manual labor, human error, and required expertise. Beyond provisioning, and configuration, backup, it automates patching and updates without any downtime or administrative action. Autonomous Data Warehouse uniquely automates performance tuning to ensure consistent high performance even as workloads change, or the number of concurrent users increases. To meet business demands, Autonomous Data Warehouse autoscales automatically with no downtime, adding only the needed resources and ensuring that customers do not pay for over-provisioned instances with a rigid T-shirt size model. In addition, it can also automatically recover from failure, reducing the need for human intervention. All this ensures that business teams can improve agility and operate data marts independently from IT, while DBAs can focus on using data to generate business value instead of performing high-cost manual tasks.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 multi-model data and multiple workloads. It includes built-in self-service tools to improve the productivity of analysts, data scientists and developers. Analysts can load and transform data by dragging and dropping rather than scripting, quickly build new business models and automatically identify data for further analysis. Both expert and citizen data scientists can build machine learning models using Python, SQL or AutoML and deploy them in the database or use REST endpoints. Developers can build data-driven applications with converged data, and use both spatial and graph analytics to deliver better results.Analyst report: Extracting Maximum Value from Your Data with Oracle Autonomous Data Warehouse (PDF)
Unlike other cloud data warehouse services, Autonomous Data Warehouse offers three deployment choices.
Autonomous Data Warehouse makes it easy to keep data safe from outsiders and insiders. It autonomously encrypts data at rest and in motion (including backups and network connections), protects regulated data, applies all security patches, enables auditing, and performs threat detection. In addition, customers can easily use Oracle Data Safe to conduct ongoing security assessments, user and privilege analysis, sensitive data discovery, sensitive data protection, and activity auditing. Autonomous Data Warehouse protects the organization and all data against data breaches, malware injections, DDoS attacks, malicious insiders, advanced persistent threats, insecure APIs, account hijacking, and more.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.
The objective of IT automation is to remove IT from the day-to-day workflows and allow the lines of business to work directly to define and mine the data that matters,” says David Floyer, CTO & Co-founder of Wikibon. “The Oracle Autonomous Data Warehouse now allows end-users to use drag-and-drop and low-code technologies to define the data requirements for a wide variety of end-user tools such as Tableau and Qlik. ADW has improved spatial, graph, and ML analytics available on-premises or in public clouds with improved real-time performance. Oracle is cool again.
David Floyer CTO & Co-founder of Wikibon
KuppingerCole has recognized Oracle’s continued innovation in database technologies, naming Oracle Autonomous Database the Overall Leader in our Leadership Compass on Enterprise Databases in the Cloud last year,” said Alexei Balaganski, Lead Analyst, KuppingerCole Analysts. “Clearly, the company did not stop there. With the unveiling of the improved Autonomous Data Warehouse, Oracle continues to deliver on its vision to democratize data management, analytics, and security for organizations of any size or industry. These new features and enhancements allow every user to access any data and obtain insights close to real-time with intelligent self-service tools. The company’s “converged database” approach ensures that all types of data are accessible at once, as opposed to the siloed nature of traditional analytics platforms. This helps businesses to avoid the exposure of sensitive information to unnecessary security and compliance risks.
Alexei Balaganski Lead Analyst, KuppingerCole Analysts
Learn how Oracle customers are using Oracle Autonomous Database to transform their businesses by redefining data warehousing through machine learning and automation
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