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Oracle Autonomous Database

Features of Oracle Autonomous Database

Because of its machine learning functionality, Oracle Autonomous Database is able to assimilate the information that it needs to take care of itself. For example, the autonomous software provisions databases on its own, finding, allocating and configuring all of the necessary hardware and software for users.

Oracle Autonomous Database also doesn’t require manual tuning to optimize performance; the technology tunes itself, including automatic creation of database indexes to help improve application performance. It also automatically applies database updates and security patches, backs up databases and encrypts data to protect information against unauthorized access.

The system patches itself on a regular quarterly schedule, although users can override this feature and reschedule the automatic patches if desired. Oracle Autonomous Database can also apply out-of-cycle security updates when necessary — for example, if Oracle releases an emergency patch to address a zero-day exploit. Additionally, the technology uses Oracle’s Database Vault tool to prevent Oracle DBAs from seeing user data and the company’s data masking feature to identify and conceal sensitive data.

Oracle Autonomous Database can scale itself up as needed; it also monitors capacity limits and bottlenecks in key system components in an effort to avoid performance problems. Updates are applied in a rolling fashion across a clustered system’s compute nodes so applications can continue to run during the process, and Autonomous Database automatically repairs itself in the event of a system failure, according to Oracle, which guarantees 99.995% uptime on the cloud service.

The technology gathers statistics as new data is uploaded, and regularly runs tests to ensure that all changes and upgrades are safe. It scans for issues across all layers of the technology stack using diagnostic tools such as ORAchk, EXAchk, OSWatcher and Procwatcher. If an error occurs, Autonomous Database collects relevant diagnostic data, analyzes logs to establish a timeline and works backward to solve the problem. For example, it can back out data errors made by users.

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