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An autonomous database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. Unlike a conventional database, an autonomous database performs all these tasks and more without human intervention.
Databases store critical business information and are essential for the efficient operation of modern organizations. DBAs are often overburdened with the time-consuming manual tasks of managing and maintaining databases. The demands of current workloads can lead to DBA errors, which can have a catastrophic impact on uptime, performance, and security.
For example, failing to apply a patch or security update can create vulnerabilities. Failing to apply the patch correctly can weaken or eliminate security protections altogether. If the database is not secure, the enterprise can be at risk for data breaches that can have serious financial repercussions and negatively impact a company’s reputation.
Business applications add new records to existing databases or use database information to create reports, analyze trends, or look for anomalies. This can cause databases to grow to many terabytes in size and become highly complex, making them even more difficult for DBAs to manage, secure, and tune for maximum performance. Databases that are slow-running or unavailable due to downtime can negatively impact employee productivity and frustrate customers.
The amount and velocity of data available to the enterprise is accelerating. This amplifies the need for efficient, secure database management that enhances data security, reduces downtime, improves performance, and is not vulnerable to human error. An autonomous database can achieve these objectives.
Information stored in a database management system can be either highly structured (such as accounting records or customer information) or unstructured (such as digital images or spreadsheets). The data may be accessed directly by customers and employees, or indirectly through enterprise software, websites, or mobile apps. Additionally, many types of software—such as business intelligence, customer relationship management, and supply chain applications—use information stored in databases.
An autonomous database consists of two key elements that align with workload types.
An autonomous database leverages AI and machine learning to provide full, end-to-end automation for provisioning, security, updates, availability, performance, change management, and error prevention.
In this respect, an autonomous database has specific characteristics.
There are several benefits of an autonomous database.
An autonomous database also allows an organization to refocus database management staff on higher-level work that creates greater business value, such as data modeling, assisting programmers with data architecture, and planning for future capacity. In some cases, an autonomous database can help a business save money by reducing the number of DBAs needed to manage its databases or by redeploying them to more strategic tasks.
Several fundamental intelligent technologies support autonomous databases―enabling the automation of mundane but important tasks such as routine maintenance, scaling, security, and database tuning. For example, an autonomous database’s machine learning and AI algorithms include query optimization, automatic memory management, and storage management to provide a completely self-tuning database.
Machine learning algorithms help companies improve database security by analyzing reams of logged data and flagging outliers and anomalous patterns before intruders can do damage. Machine learning can also automatically and continuously patch, tune, back up, and upgrade the system without manual intervention, all while the system is running. This automation minimizes the possibility that either human error or malicious behavior will affect database operations or security.
In addition, autonomous databases have some specific capabilities.
With an autonomous database, developers can quickly build scalable and secure enterprise applications from data housed in a preconfigured, fully managed, and secure environment.
Autonomous databases offer many benefits. When you're ready to evaluate the offerings available to your organization, look for the following key features.
Data is being generated today at a rate that is fast outpacing how quickly it can be manually managed and processed to efficiently and securely deliver business-critical insights. Because of their intelligent automation capabilities, autonomous databases offer enterprises many advantages over traditional databases. The expectation is that enterprises will increasingly migrate to this database model to enjoy these advantages, maintain a competitive edge, and gain the ability to refocus IT efforts on innovation rather than database management.