I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.
Oracle also has a good argument for being considered for new applications. Oracle Database can used for operational, analytic or mixed workloads; on-premises or in the cloud; and on general-purpose or dedicated hardware. Additionally, the company’s wider database portfolio has offerings to address the requirement for open-source databases, NoSQL key value data storage, and data lake options for big data storage and processing. Oracle can also point to significant differentiation, thanks to its investment in autonomous database functionality, designed to reduce the complexity of configuring, managing and operating database management systems.
Unveiled in 2017, Oracle Autonomous Database is a managed service that leverages machine learning to automatically optimize, scale, tune, patch and secure Oracle’s database management system. Oracle Autonomous Database is described as a converged database offering that is optimized for transaction processing and mixed workloads (Autonomous Transaction Processing) or analytic workloads (Autonomous Data Warehouse). For transaction processing and mixed workloads, Autonomous Database is pre-configured for row format, indexes and data caching. Hybrid data processing for mixed workloads is enabled by In-Memory Column Store, which was first introduced with Oracle Database 12c to improve real-time analytics and mixed workloads by replicating data from the disk-based row-store into a memory-based column-store for analytic and reporting queries. In-Memory Column Store was enhanced with the more recent Oracle Database 21c to improve automation. For analytic workloads, Autonomous Database is pre-configured for columnar format, partitioning and large joins. Despite the emergence of a variety of alternatives, relational databases remain the most popular analytic data platform, with almost three-quarters of participants in Ventana Research’s Analytics and Data Benchmark Research using relational databases in their analytics efforts.
In addition to automatically configuring for the defined workload, Autonomous Database has a variety of other automation features designed to reduce the burden for database administrators, including automated provisioning, indexing, partitioning, scaling and cloning. There are also a variety of automated security features for encryption, auditing, privacy and patching as well as automated capabilities for backup, high-availability and incident detection and resolution.
Autonomous Database is available on Oracle Cloud Infrastructure (in shared or dedicated deployments) as well as in a customer’s own datacenter, via Exadata Cloud@Customer or Dedicated Region Cloud@Customer and in hybrid cloud environments. In addition to Autonomous Database, Oracle also continues to offer Oracle Database for deployment on Exadata or a customer’s chosen on-premises or cloud infrastructure as well as Oracle Enterprise Database Service and Oracle Exadata Cloud Service. The company also offers Oracle NoSQL Database on Oracle Cloud Infrastructure for key-value workloads, as well as offerings based on the MySQL open-source database it acquired along with Sun Microsystems in 2009. Oracle expanded MySQL-related offerings in recent years with the launch of the Oracle MySQL HeatWave cloud service on Oracle Cloud Infrastructure. Oracle MySQL HeatWave is also positioned to run both transactional and analytic workloads, and includes a hybrid columnar query-processing engine as well as MySQL Autopilot for automated provisioning, data loading and query execution. Oracle recently enhanced the offering with the addition of in-database machine learning capabilities including automated model training, model and inference explanations and hyper-parameter tuning.
Oracle’s identity remains closely associated with the database management system with which it first established itself. The database landscape has evolved considerably in recent years. So too, has Oracle. The larger data platform portfolio includes Oracle Cloud Infrastructure Data Lakehouse which we will address in a future Analyst Perspective. Complementary to that, Oracle’s database services portfolio offers a range of options to address different data models and application workloads, as well as innovative and differentiating functionality, particularly in terms of automation. As such, the company’s database services portfolio should at the very least be evaluated for any data platform initiative.
Regards,
Matt Aslett