The main purpose of a cloud data platform is to give an organization an easier way to use its data, while also securing that data, managing it, and offering an integrated view of it. Cloud data platforms combine:
Given the rapid growth in data, as well as the different types of data used in making business decisions, cloud data warehouses must provide flexibility and options that a variety of organizations can take advantage of. Everyone from large, multinational corporations to small enterprises are investigating or using these cloud-based data warehouses because they can be reliable and affordable partners in handling data management.
Streaming, batch data, both on-premises and in the cloud
Autonomous, self-driving, self-securing, self-repairing
Object storage-based data lake, integrated access with data warehouse
ML-based analytics and visualization; Automatic narration
Machine learning, general purpose, and in-database
The best cloud data platform should provide a complete, integrated solution for:
Customers can ingest any data batch, streaming or real-time, store in data warehouse or data lakes, catalog and govern, visualize and analyze, and build and deploy machine-learning solutions.
With an integrated solution, customer can leverage security policies across the data warehouse and data lake, as well as seamlessly query the data lake and data warehouse together. Built-in support for multimodel data and multiple workloads such as analytical SQL, machine learning, graph, and spatial in a single database instance reduces the integration complexity and administration that is required with other providers, while still offering support for third-party integration and analytics tools.
With a cloud data platform, organizations can deploy in minutes, instead of months. Oracle provides web-based user interfaces for self-service provisioning, data loading, and data analysis. It takes only a few minutes to provision and start analyzing data, no integrations are required.
Existing Oracle Database customers can maintain the same data models and tools, and ETL processes make it simple to modernize. While it's important to consider, getting started is about much more than just time to provision a functioning data warehouse. Existing applications, tools, ETL processes, and much more all need to work with the new cloud data platform. Because our Cloud platform is based on the same on-premises database in widespread use, migration for existing database customers is much simpler.
Having a cloud data platform that provides the necessary tools to develop, integrate, monitor, and secure applications—as well as the ability to use analytics to create accurate, actionable, and transformational insights—can be a challenge, and not every platform can do it. That’s why a secure approach to the cloud is key, with embedded security deep within each layer of the cloud (down to the chip layer) and separate cloud security services that customers can build into their cloud applications.
Autonomous management enables customers to run high-performing, highly available, and secure data warehouses while eliminating administrative complexity and reducing costs. This makes it simple, for example, for individual lines of business to set up their own dedicated data mart without having to rely on IT to provision and operate it. Oracle Autonomous Data Warehouse automates provisioning, configuring, securing, tuning, scaling, backing up, and repairing data warehouses.
Cloud data platforms should have analytics tools that are powerful and easy to use, to enable better customer service and create new revenue streams. We provide built-in analytics tools like spatial and graph with Autonomous Data Warehouse, easy integration with Oracle Analytics Cloud, support for other popular business intelligence (BI) tools, and built-in services to build and deploy machine-learning models. This comprehensive set of tools and services enables customers to create agile organizations that move faster.
Single-purpose databases, or purpose-built databases as they are often as known, are engineered to help solve a narrow set of problems. Their simplicity means they do a few things very well, but other things not at all. For example, a lot of single-purpose databases scale well, because they offer no strong consistency guarantees.
At first, single-purpose databases seem like a good option, because developers get exactly what they need to begin a project. However, development requirements change mid-project, and unforeseen business needs crop up, which leaves developers with a tough decision: start from scratch with another single-purpose database to accommodate the new requirements, or work around the limitations of the original single-purpose database, which adds unnecessary complexity. And tasks like operational reporting become very hard or even impossible with needed data distributed in multiple formats and different specialty databases.
The converged database has native support for all modern data types built into one product. Converged databases support spatial data for location awareness, graph data for relationship modeling, JSON for document stores, IoT for device integration, in-memory technologies for real-time analytics, and traditional relational data. By supporting these different data types, a converged database can run all kinds of workloads, from IoT and blockchain to analytics and machine learning. And by integrating new data types and workloads within a converged database, you can support various workloads and types of data more simply, without the need to manage and maintain multiple systems, or provide unified security across them. With support for machine learning algorithms and graph data in the same database, you can easily perform feature engineering with graph analytics and then use that data to augment your machine learning data. This makes it easier and faster to develop data-driven apps.
A cloud data management platform with a broad and deep portfolio across applications, platforms, and infrastructure gives your business the tools and ability to build your own path to a successful cloud. As a result, you spend less on IT maintenance and more on real innovation—knowing that your partner has the tools to address all your needs.
Choice across cloud deployment options provides organizations with total control and flexibility. Our Cloud data platform allows customers to deploy and manage their respective applications on their own private cloud, or move those workloads to the public cloud. This is a seamless migration through the use of standard technologies (same standards, same products, and unified management). Additionally, our Cloud@Customer solution provides additional options by allowing organizations to bring the power of our cloud within their respective firewalls.
We are changing how data is managed with the introduction of the world's first self-driving database. Our database technology automates data management to provide unprecedented availability, performance, and security through the integration of artificial intelligence and machine learning. The Autonomous Database includes three key elements: Database-optimized infrastructure as a service, automated database operations, and policy-driven workload optimization and machine learning. This solution enables provisioning, patching, upgrading, and backing up online; monitoring, scaling, diagnosis performance, tuning, and optimizing; and automatic handling of failures and errors. The Autonomous Database comes with JSON, machine learning, graph analytics, and spatial analytics, which means users don't have to move data and are able to work with the same database to fulfill multiple needs.
The cloud data platform is an integrated solution that supports machine learning, third-party analytics, and ISV applications. We offer a single solution that provides self-driving integration, data warehouse, data lakes, analytics services, and data science to enable organizations to get the most value from their data. This modern data warehouse simplifies every aspect of data, including ingestion, transformation, curation, data discovery, and analysis. Using this tool, organizations are able to extract the highest value from their data in order to better serve customers today while looking to business innovation in the future.