Oracle Exadata X9M was released in 2021 as a hardware/software platform for Oracle databases on prem and in the cloud. This note addresses its implications for database customers.
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HARBR (HARBR Group Ltd.) is a cloud-based data commerce company that provides a platform for creating, managing, and distributing data products. This note discusses how the platform discards old ideas and enables business breakthroughs via a collaborative use of data and analytics, and advanced ideas about the value, productization and monetization of data.
This note discusses unique autonomous database services that are fully automated and self-managing at the customer’s location. These run on a new generation of high-performance Exadata database machines and are especially important for customer who face demanding database sovereignty and compliance, security, performance, and other relevant issues.
Cloud database services are popular for speed, agility, elasticity, and cost benefits. Yet many customers also need flexible deployment to address sovereignty, compliance, service levels, and diverse workloads. Oracle may offer the broadest deployment range, with Cloud@Customer blending cloud and on-premises advantages. It’s the only product addressing all four key database deployment challenges.
It’s time to rethink the Hadoop data store. This report outlines core challenges, presents a DataOps-driven approach for agility, and offers strategic guidance. Most big data stores—on Hadoop or in the cloud—are disorganized “data swamps” with duplicate data, poor quality, and security risks. These problems grow with data volumes. On-prem Hadoop requires costly node additions due to triple replication, while cloud storage often brings hidden fees and access slowdowns.
With an autonomous database in the cloud, Oracle has made databases much simpler to provision, secure, develop, manage and operate. It offers a major leap in ease of use and time-to-value. Running on Oracle Cloud and Exadata with workload optimizations, it’s significantly easier to use than previous versions and will become one of the most user-friendly cloud databases available.
Here are several features of the Oracle approach that set it apart from other cloud database services.
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An autonomous cloud data warehouse requires a minimum of physical database design, tuning, and operational management from humans. Today, most of this is taken over by machine learning software. This research note discusses this approach and a hands-on experiment with Oracle’s product.
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Oracle extends the autonomous cloud database idea to JSON-centric document databases with an offering that is another step in its converged database strategy. This cloud-based NoSQL capability is part of the autonomous database family enabling developers to store and manage JSON data natively, access it via a document API, and analyze it via SQL.
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Oracle Autonomous Transaction Processing (ATP) is a cloud database designed for operational data use and complements Oracle’s Autonomous Data Warehouse (ADW). Both self-driving versions enable customers to focus only on logical design and application development. ATP simplifies operations, reduces staffing needs, lowers costs, and accelerates time to market. It also offers true elastic scaling without downtime.
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– Autonomous Databases
– Data Strategy
– Data Architecture
Executives seek faster, more effective ways to turn analytics into action. A model, like a churn predictor, only delivers value when integrated into customer-facing systems. Yet data scientists are often far removed from business users, causing long delays before insights are applied. To maximize impact, companies must deploy and reuse analytics quickly and broadly. Teradata AppCenter addresses a centralized platform where users can access pre-built analytics or assemble new ones—enabling rapid deployment across the enterprise, much like an app store for analytics.
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