What Is an Autonomous Database?

Autonomous Database defined

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.

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 can now focus on more important tasks, including data aggregation, modeling, processing, governance strategies, and helping developers use in-database features and functions with minimal changes to 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.

The Developer Advantage

With an autonomous database, developers can quickly build scalable and secure enterprise applications from data housed in a preconfigured, fully managed, and secure environment.

Choosing an Autonomous Database

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

  • Auto-Provisioning
    Automatically deploys mission-critical databases that are fault-tolerant and highly available. Enables seamless scale-out, protection in case of a server failure, and allows updates to be applied in a rolling fashion while apps continue to run.
  • Auto-Configuration
    Automatically configures the database to optimize for specific workloads. Everything from the memory configuration, the data formats, and access structures are optimized to improve performance. Customers can simply load data and go.
  • Auto-Indexing
    Automatically monitors workload and detects missing indexes that could accelerate applications. It validates each index to ensure its benefit before implementing it and uses machine learning to learn from its own mistakes.
  • Auto-Scaling
    Automatically scales compute resources when needed by workload. All scaling occurs online while the application continuously runs. Enables true pay per use.
  • Automated Data Protection
    Automatically protects sensitive and regulated data in the database, all via a unified management console. Assesses the security of your configuration, users, sensitive data, and unusual database activities.
  • Automated Security
    Automatic encryption for the entire database, backups, and all network connections. No access to OS or admin privileges prevents phishing attacks. Protects the system from both cloud operations and any malicious internal users.
  • Auto-Backups
    Automatic daily backup of database or on-demand. Restores or recovers a database to any point in time you specify in the last 60 days.
  • Auto-Patching
    Automatically patches or upgrades with zero downtime. Applications continue to run as patching occurs in a round-robin fashion across cluster nodes or servers.
  • Automated Detection and Resolution
    Using pattern recognition, hardware failures are automatically predicted without long timeouts. IOs are immediately redirected around unhealthy devices to avoid database hangs. Continuous monitoring for each database automatically generates service requests for any deviation.
  • Automatic Failover
    Automatic failover with zero-data loss to standby. It’s completely transparent to end-user applications. Provides 99.995% SLA.

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.