What Is an Autonomous Database?

Why Use an Autonomous Database

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.

Types of Data Stored in Databases

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.

Components of an Autonomous Database

An autonomous database consists of two key elements that align with workload types.

◉ A data warehouse performs numerous functions related to business intelligence activities, and uses data that’s been prepared in advance for analysis. The data warehouse environment also manages all database lifecycle operations, can perform query scans on millions of rows, is scalable to business needs, and can be deployed in a matter of seconds.

◉ Transaction processing enables time-based transactional processes such as real-time analytics, personalization, and fraud detection. Transaction processing typically involves a very small number of records, is based on predefined operations, and allows for simple application development and deployment.

How an Autonomous Database Works

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.

◉ It is self-driving. All database and infrastructure management, monitoring, and tuning processes are automated. DBAs are still needed for tasks such as managing how applications connect to the a and helping developers use the in-database features and functions without their application code.

◉ It is self-securing Built-in capabilities protect against both external attacks and malicious internal users. This helps eliminate concerns about cyberattacks on unpatched or unencrypted databases.

◉ It is self-repairing. This can prevent downtime, including unplanned maintenance. An autonomous database can require fewer than 2.5 minutes of downtime per month, including patching.

Benefits of an Autonomous Database

There are several benefits of an autonomous database.

◉ Maximum database uptime, performance, and security―including automatic patches and fixes

◉ Elimination of manual, error-prone management tasks through automation

◉ Reduced costs and improved productivity by automating routine tasks

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.

Intelligent Technologies Support Autonomous Databases

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.

◉ Easy scalability. A cloud-based database server can expand or reduce its compute and memory resources instantly, as needed. For example, a company could scale up from 8 cores of database computing to 16 cores for end-of-quarter processing, and then scale down to the less-expensive 8 cores afterward. In fact, all compute resources could be shut down over the weekend to reduce costs, and then be started up again on Monday morning.

◉ Seamless database patching. Many data breaches are enabled by system vulnerabilities for which a security or vulnerability patch is already available but not yet applied. An autonomous database prevents this issue by automatically rolling patches against the cloud servers in a sequence designed to eliminate business downtime.

◉ Integrated intelligence. An autonomous database integrates monitoring, management, and analytics capabilities that leverage machine learning and AI techniques. The goal is to automate database tuning, prevent application outages, and harden security across the entire database application.

Deployment Options for Autonomous Databases

There are two options for deploying an autonomous database.

◉ Serverless deployment. In serverless deployment, multiple users share the same cloud infrastructure resources. Serverless deployment is the simplest option; it requires no minimum commitment and users can take advantage of quick data provisioning and application development. Users also enjoy independent compute and storage scalability. In this deployment model, users are responsible for database provisioning and management while the provider takes care of infrastructure deployment and management responsibilities.

◉ Dedicated deployment. Dedicated deployment allows the user to provision the autonomous database within a dedicated (unshared) cloud infrastructure. This deployment model has no shared processor, memory, network, or storage resources. Dedicated deployment offers greater control and customization over the entire environment and is ideal for users who want to tailor their autonomous database to meet specific organizational needs. Additionally, dedicated deployment allows for an easy transition from on-premise databases to a fully autonomous and isolated private database cloud.

Choosing an Autonomous Database

Autonomous databases offer many benefits to businesses. When you’re ready to evaluate the offerings available to your organization, look for four key features.

◉ Serverless deployment. In serverless deployment, multiple users share the same cloud infrastructure resources. Serverless deployment is the simplest option; it requires no minimum commitment and users can take advantage of quick data provisioning and application development. Users also enjoy independent compute and storage scalability. In this deployment model, users are responsible for database provisioning and management while the provider takes care of infrastructure deployment and management responsibilities.

◉ Dedicated deployment. Dedicated deployment allows the user to provision the autonomous database within a dedicated (unshared) cloud infrastructure. This deployment model has no shared processor, memory, network, or storage resources. Dedicated deployment offers greater control and customization over the entire environment and is ideal for users who want to tailor their autonomous database to meet specific organizational needs. Additionally, dedicated deployment allows for an easy transition from on-premise databases to a fully autonomous and isolated private database cloud.

The Future of Autonomous Databases

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.