New Database Innovations Deliver a Single Database that Supports all Data

The future is data driven, and effective use of data will increasingly determine a company’s competitiveness. Unlocking the full value of an enterprise’s data requires a new generation of data driven apps. Oracle makes it easy to create modern data driven apps utilizing a single database engine which supports the most suitable data model, process type, and development paradigm for a wide variety of business requirements. We enable our customers to easily run many kinds of workloads against the same data. In contrast, other cloud providers require dozens of different specialized databases to handle different data types. Having to deploy multiple single-purpose databases leads additional challenges. Having to implement multiple different database engines will increase complexity, risk, and cost because each database introduces its own security model, its own set of procedures for implementing high availability, its own scalability capabilities, and requires separate skillsets to operate.

Much in the way a single smartphone is now a camera, a calendar, a platform for entertainment, and a messaging system, the same idea applies to Oracle’s converged database engine. With Oracle Database, enterprises are no longer forced into purchasing multiple individual single-purpose databases, when all they need is one converged database engine that handles everything.

• Oracle Machine Learning for Python (OML4Py): Oracle Machine Learning (OML) inside Oracle Database accelerates predictive insights by embedding advanced ML algorithms which can be applied directly to the data. Because the ML algorithms are already collocated with the data, there is no need to move the data out of the database. Data scientists can also use Python to extend the in-database ML algorithms.
• OML4Py AutoML: With OML4Py AutoML, even non-experts can take advantage of machine learning. AutoML will recommend best-fit algorithms, automate feature selection, and tune hyperparameters to significantly improve model accuracy.
• Native Persistent Memory Store: Database data and redo can now be stored in local Persistent Memory (PMEM). SQL can run directly on data stored in the mapped PMEM file system, eliminating IO code path, and reducing the need for large buffer caches. Allows enterprises to accelerate data access across workloads that demand lower latency, including high frequency trading and mobile communication.
• Automatic In-Memory Management: Oracle Database In-Memory optimizes both analytics and mixed workload online transaction processing, delivering optimized  performance for transactions while simultaneously supporting real-time analytics, and reporting. Automatic In-Memory Management greatly simplifies the use of In-Memory by automatically evaluating data usage patterns, and determining, without any human intervention, which tables would most benefit from being placed in the In-Memory Column Store.
• Native Blockchain Tables: Oracle makes it easy to use Blockchain technology to help identify and prevent fraud. Oracle native blockchain tables look like standard tables. They allow SQL inserts, and inserted rows are cryptographically chained. Optionally, row data can be signed to ensure identity fraud protection. Oracle blockchain tables are simple to integrate into apps. They are able to participate in transactions and queries with other tables. Additionally, they support very high insert rates compared to a decentralized blockchain because commits do not require consensus.
• JSON Binary Data Type: JSON documents stored in binary format in the Oracle Database enables 4X faster updates, and scanning up to 10X faster.

Oracle’s continuing to lead the industry in delivering the world’s most comprehensive data management solutions, including the industry’s first and only self-driving database, Oracle Autonomous Database. The company was recently named the leader in “The Forrester WaveTM: Translytical Data Platforms, Q4 2019 report which cites that, “unlike other vendors, Oracle uses a dual-format database (row and columns for the same table) to deliver optimal translytical performance,” and that “customers like Oracle’s capability to support many workloads including OLTP, IoT, microservices, multi-model, data science, AI/ML, spatial, graph, and analytics.” 

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Future Product Disclaimer

The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.