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What is a Cloud Data Platform?

Cloud Data Platform

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:

  • Easier and faster scalability
  • Data warehousing
  • Data lakes
  • Data engineering
  • Data science
  • Data application development
  • Data exchange (securely sharing and consuming shared data)

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.

Modern Data Warehouse Components


Integration

Streaming, batch data, both on-premises and in the cloud


Data Warehouse

Autonomous, self-driving, self-securing, self-repairing


Data Lake

Object storage-based data lake, integrated access with data warehouse


Analytics

ML-based analytics and visualization; Automatic narration


Data Science

Machine learning, general purpose, and in-database

Benefits of an integrated cloud data platform

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.



Easy to start

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.

Easy to operate and secure

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.

Easy to analyze

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.

To modernize, and get the most from your data, customers need 4 things:

Complete Integrated Solution

Easy to Start

infographic image

Easy to Operate and Secure

Easy to Analyze

Cloud data platform architecture

What sets an effective data management platform in the cloud apart is its ability to respond to changes, while also being able to continuously adapt. Here are some other key features that should be part of any modern cloud data platform architecture:

  • Elasticity: The architecture of a cloud data platform must be elastic to meet constantly changing demands. Organizations need a platform that can scale up or down easily, while still having predictable pricing. There are three key factors to consider when evaluating elasticity:

    1. Does scaling apply to all workloads, or are there restrictions (like scaling up to support read-only workloads, not writes)?
    2. Is any down time required while scaling?
    3. When adding capacity, is it possible to add in small increments or is it required to add a new, large cluster?

  • Automation: A modern data architecture must be automated, so that it can detect changes to data, and respond accordingly. This is why many of the best cloud data platforms use machine learning and artificial intelligence to identify connections, make recommendations, and send alerts.
  • Flexibility: Cloud data platforms need to support the many and varied needs of a modern business, whether it’s handling multiple queries at once, processing data, running applications, or more. It’s crucial to be able to perform multiple, concurrent tasks with little downtime.
  • Lower cost: Having fast and easy access to your data can save time and money down the road, and with pay-as-you-go pricing models offered by some cloud data platforms, you’ll only pay for what you use.

Cloud data platform use cases

  • Build a data mart: Analysts need an efficient way to consolidate data from multiple spreadsheets and other flat-file data sources into a trusted, maintainable, and query-optimized source. With a cloud data platform, they can load and optimize data from multiple sources into a centralized data warehouse so departments can analyze the data and gain actionable insights.
  • Integrated with business-critical applications: Load and optimize data from Oracle E-Business Suite and other sources into a centralized data warehouse location for analysis so departments can gain actionable insights—and ensure there’s a simple way for results to be operationalized and fed back into applications to enable data-driven decisions.
  • Build a data warehouse: Data is often distributed in multiple systems across the enterprise and can't be easily integrated and analyzed to produce actionable insights. Enrich enterprise application data with raw data and event data to produce predictive insights.
  • Integrate a data lake and data warehouse: Combine the abilities of a data lake and a data warehouse to process streaming data and a broad range of enterprise data resources and leverage the data for business analysis and machine learning.
  • Process and analyze streaming data with machine learning: Process streaming event and log data for predictive maintenance. Apply advanced analytics and data science capabilities to understand the context for an actionable event, gain insight, and create a response. Oracle Autonomous Database integrates well with Oracle Analytics Cloud, so you can build, test, and deploy applications, while also taking advantage of embedded spatial and graph analytics.

Converged database

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.

Cloud data platform development

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.

Watch the video: Oracle Cloud Data Platform Services vs. Amazon AWS (11:59)

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.