The autonomous database has created a lot of buzz in recent years and has grown significantly. ReportLinker predicts the Global Autonomous data platform market will reach $2,316 million by 2027. Yet, not many people understand its full capability or how the technology works.
The last two decades have seen a lot of technological development and advancement in data management. However, 2008 marked significant progress due to the NoSQL wave that included big data.
Autonomous data management is a game-changing technology that introduced self-driving database technology to the world. It requires no traditional performance tuning by database administrators and developers. Instead, organizations merely define tables, load data, run queries, and the database does the rest.
An autonomous database refers to a cloud-based database that automates database tuning, backups, updates, security, and other routine management tasks using machine learning. Traditionally, a database administrator (DBA) performed all these tasks, but, unlike a conventional database, the autonomous database can perform these tasks without human intervention.
There are two types of databases based on the information stored in database management systems. These are:
Stored data can be directly accessed by employees or customers or indirectly accessed through enterprise software, mobile apps, and websites. Moreover, several types of software use information stored in databases. These include business intelligence, supply chain applications, and customer relationship management.
Autonomous databases consist of two primary elements that align with workload types:
The rise of autonomous databases came from necessity – the need to manage business data properly. Databases store critical information and are vital for the efficient operation of modern organizations.
Unfortunately, database administrators are often overburdened with time-consuming manual tasks of maintaining and managing databases. Moreover, the demands of workloads can lead to errors, which could have a disastrous impact on uptime, security, and performance.
For example, suppose a DBA fails to apply a security update or patch. In that case, it could create vulnerabilities while failing to apply the patch or update, eliminating or weakening security protections. Unsecure databases put enterprises at risk for data breaches that could lead to serious financial repercussions and harm the company’s reputation.
Business apps add new records to databases and use the stored information to create reports, look for anomalies, and analyze trends. In turn, it could grow databases into many terabytes and make them highly complex. As a result, DBAs find it even more challenging to manage, secure, and tune them for maximum performance.
Unfortunately, slow-running databases or those unavailable due to downtime negatively affect employee productivity, frustrate customers, and impact the company’s bottom line. Additionally, the increasing amount of big data available for enterprises amplifies the need for efficiency.
Big data also necessitates secure database management to enhance data security, improve performance, reduce downtime, and reduce human error. Hence, there’s a need for an autonomous database to achieve these objectives. Presently, there are several existing automated database examples.
High-performance in-memory computing has changed how enterprises work and enterprises currently split data into two distinct databases for performance reasons. Disk-based row-oriented systems are best for operational data, while column-oriented systems are suitable for analytics. Organizations often keep analytical databases in memory, but they can also mix them with disk-based storage media.
Organizations do not store analytical and transactional data in the same database. Instead, analytical data resides in a separate data house replicated in batch jobs. Consequently, it’s impossible to have flexible real-time reporting, so leaders must make decisions with insufficient information in short time frames.
However, this is about to change with the advent of autonomous data management systems. In addition, hardware architectures have developed significantly in the last few years. Multi-core architectures and low-cost main memory with significant storage space are emerging software breakthroughs.
It’s now possible to store large data sets of entire companies in the main memory. It’s also possible to achieve orders of magnitude faster performance compared to disk-based systems. As a result, traditional disks will become secondary backup devices similar to tape drives.
In-memory computing and hybrid databases use row and column-oriented storage where applicable, meaning it’s now possible to unify transactional and analytical processing.
An autonomous database leverages machine learning and artificial intelligence to provide end-to-end automation for tuning, provisioning, availability, security, performance, error prevention, change management, and updates.
An autonomous database has the following specific characteristics:
Many intelligent technologies support autonomous databases, enabling the automation of mundane but essential tasks like security, scaling, database tuning, and routine maintenance. For example, autonomous databases have machine learning and artificial intelligence algorithms such as query optimization, storage management, and automatic memory management – providing a completely self-tuning database.
What’s more, machine learning algorithms help organizations improve their database security by analyzing logged data and flagging anomalous patterns before intruders gain access and damage the system.
Machine learning can automatically and continuously patch, upgrade, tune, and back up the system with minimal manual intervention while the system runs. The automation minimizes the possibility of human error and malicious behavior affecting database operations or security.
Autonomous data management has numerous use cases depending on the organization’s needs. Three common uses are:
Development Testing (DevTest) is an essential use case for the public cloud. Organizations form DevOps teams where developers and operating personnel work together to build, test, improve, and troubleshoot applications, forming part of a continuous process. Autonomous data management comes in handy in application development and testing.
Organizations can leverage autonomous databases as staging environments for testing their upgrade processes or new database features like pluggable databases or portable tablespaces.
Autonomous databases are ideal for data warehouse workloads, especially when different and geographically distributed teams want access to analysis services. In addition, they reduce the cost and complexity of infrastructure data management, leaving analysts to focus on value extraction from the data. Moving a data warehouse to the cloud gives users remote access and allows entire teams to use data warehouse resources from different locations.
Autonomous databases have specific capabilities, such as:
Cloud-based database servers can expand or reduce their compute and memory resources instantly. For example, an organization could scale up from eight database computing cores to sixteen for its end-of-quarter processing before scaling down to eight, which is less expensive. Additionally, it’s possible to shut down all computing resources over the weekend to reduce operating costs and start them back up on Monday.
System vulnerabilities enable data breaches if database administrators haven’t applied security or vulnerability patches. An autonomous database automatically rolls patches against cloud servers sequentially to eliminate business downtime and prevent security breaches.
Autonomous databases integrate management, monitoring, and analytical capabilities to leverage machine learning and artificial intelligence techniques. Autonomous data intelligence aims to automate database tuning, tighten security, and prevent application outages across the database application.
Developers can use autonomous databases to build secure and scalable enterprise applications using data housed in pre-configured, secure, and fully-managed environments.
An autonomous database offers numerous benefits for organizations. Therefore, it’s essential to evaluate the options available before choosing a self-driving database. Features to consider include:
Auto-provisioning automatically deploys mission-critical, fault-tolerant, and highly available databases. It also enables a seamless scale-out, allows updates in a rolling fashion as apps continue running, and ensures protection during a server failure.
As its name suggests, auto-configuration automatically configures databases to optimize for specific workloads. It optimizes everything, including the memory configuration, access structures, and data formats. Therefore, customers can just load data and go.
Auto-scaling automatically scales compute resources when workloads need it. Furthermore, all scaling occurs as the application continually runs, enabling actual pay-per-use.
Auto-indexing automatically monitors workloads and can detect missing indexes that could affect applications. It validates the indexes before implementing them and then learns from its mistakes using its machine learning capability.
5. Automated Security
A self-driving database includes automated encryption for the entire database, network connections, and backups. In addition, restricted access to admin privileges and the operating system prevents phishing attacks and protects the system from cloud operations or malicious internal users.
6. Automated Data Protection
Automated data protection automatically protects regulated data stored in the database via a unified management console. It assesses the security of several things, including the configuration, sensitive data, unusual database activities, and users.
7. Automatic Backups
A reliable autonomous database system comes with on-demand and automatic daily backups. It should restore or recover a database to any specified point in the last 60 days.
8. Automatic Failover
Does the database allow automatic failover to standby mode with zero-data loss? It should be entirely transparent for end-user applications and provide 99.995% service level agreement.
9. Automated Detection and Resolution
An autonomous database uses pattern recognition to predict hardware failures without long timeouts automatically. Additionally, continuous monitoring should automatically generate service requests for any deviation.
The autonomous system database has automatic upgrades and patches with zero downtime. Applications continue running as patching occurs across cluster nodes or servers.
An autonomous data warehouse refers to a cloud service providing fully-managed and regularly-updated data warehousing capabilities. It’s an automated data management system designed to enable and support analytics and other business intelligence activities.
Data warehouses perform queries and analyses, often containing large historical data sets. Data warehouse data comes from various sources, including transaction applications and application log files.
Data warehouses centralize and consolidate large data amounts from multiple sources. Therefore, organizations require analytical capabilities to derive valuable business insights from the stored data to improve decision-making. Eventually, it builds a historical record that becomes invaluable to business analysts and data scientists. These capabilities make data warehouses the “single source of truth” for organizations.
A traditional data warehouse consists of:
An autonomous data warehouse contains the above features plus additional sophisticated analytical applications to generate actionable information using data science, machine learning, artificial intelligence algorithms, and spatial features for better data analysis at scale.
A cloud data warehouse uses cloud services to ingest and store data pooled from disparate sources. Although on-premises data warehouses have several advantages - such as improved governance, security, better latency, and data sovereignty - they are not as elastic as autonomous data warehouses. They also require complex forecasting when determining how to scale for future needs.
Cloud data warehouses are the way of the future because they offer these advantages:
The best cloud data warehouses are self-driving and fully managed, allowing even beginners to create and use them with only a few clicks. In addition, organizations migrating to the cloud should run their cloud data warehouses on-premises and behind their data center firewall for security and regulatory compliance.
Autonomous data protection refers to ensuring data security on a self-driving database. Autonomous database technology has proven more secure than traditional databases for several reasons, including:
Organizations store their critical data in databases, while malicious actors attempt to attack and gain access to the data from different perspectives. Hackers are inventive, have the time, and invest their resources and efforts in exploiting vulnerabilities in database applications to steal data, attack the network, access raw data files, and encrypt files for a ransomware attack.
Therefore, data security remains the primary concern for enterprise customers looking to move their backups and storage to the cloud. They want a solution that keeps their data secure and meets compliance requirements.
Providers are engaging with independent auditors and external assessment entities to meet industry-specific and international compliance standards for cloud service deployments (e.g., ISO 27001, PCI DSS, SOC1, SOC2, HIPAA, and FedRAMP).
Bringing an organization’s disparate data together is one of the biggest challenges facing enterprise data management. While no organization ever wants to silo its data, some data naturally does result in exactly that. For example, running multiple environments (e.g., cloud apps and an on-premises system) while migrating workloads to a cloud computing solution leads to data siloing.
Autonomous cloud data integration is a unified offering of process automation and application integration that significantly speeds up cloud integration activities. The provider layers an autonomous integration capability on the existing integrated cloud solutions. As a result, autonomous integration works seamlessly with different products as a fully-managed hybrid platform.
An autonomous Integration cloud service leverages machine learning and artificial intelligence to bring an organization’s cloud and non-cloud data into a warehouse (single point of truth) quickly and easily. Organizations can then seamlessly monitor and manage their data remotely.
What’s more, autonomous data integration is low code, making it easier for line-of-business (LOB) managers and supervisors to set up their integrations quickly and securely.
The autonomous cloud transforms information technology from manual operations to no operations (NoOps), autonomous processes. The NoOps concept means an automated and abstracted IT environment that requires no operations team to manage it.
This differs from outsourcing IT operations, where an organization moves to the cloud or SaaS and expects their vendor to run operations. Instead, NoOps requires multiple technologies and a reworking of IT processes and workflows to have automation, machine learning, and artificial intelligence handle repetitive, mundane, and higher-level tasks that IT staff do.
Therefore, the autonomous cloud uses AI, machine learning, and advanced automation for native cloud environments and DevOps practices to achieve automated operations and continuous software delivery pipelines.
An autonomous database is simply a cloud database that utilizes machine learning to automate database optimization, backup, security, routine administrative tasks, and updates traditionally done by database administrators. However, unlike traditional databases, the new autonomous databases perform these tasks without human intervention.
The autonomous data warehouse cloud is the next-generation cloud service based on self-driving database technology, which delivers machine learning with unprecedented performance, ease of deployment, and reliability.
Unique functions of suitable autonomous cloud management include:
The autonomous database is an unprecedented self-driving, self-repairing, and self-securing database that provides a level of reliability and performance that manually-managed databases cannot deliver. In addition, it performs better, is more available, and is easier to run than a manually-managed, on-premises database.
However, autonomous data management does not mean that IT teams will become redundant in the future and relinquish their database oversight role. Instead, automation satisfies the immediate line-of-business needs of decision-makers, leaving the IT team to serve a consultative role and engage deeply with other organizational units to innovate and create new strategic solutions.
Ultimately, autonomous data management helps foster a collaborative environment across the organization.