Welcome
The data explosion in the digital economy presents both an opportunity and a challenge. Intelligently managed and analyzed in a timely manner, data can become the most valuable asset to help uncover new business opportunities and accelerate innovation. Fragmented data and infrastructure, slow access to data, risky manual processing, and expensive resources make it prohibitive to monetize data. IT organizations are caught in a seemingly impossible situation, as the business demands more real-time data and more agility while IT budgets stay flat.
Businesses and IT need the right technology platform to monetize data.
Oracle continues to be a market and technology leader in data management delivering leading edge innovations for both IT and business. Recognized by Gartner again as a leader positioned highest for Ability to Execute in Data Management Solution for Analytics. Oracle’s ground breaking Autonomous Database Cloud delivers game changing innovations to enable businesses to transform how data is managed and analyzed. Oracle Autonomous Database Cloud uses machine learning to eliminate human labor, human error, and manual tuning. The result? Unprecedented availability, high performance, and security, all for a much lower cost. The world’s first autonomous database cloud is:
- Self-Driving: Provides continuous adaptive performance tuning based on machine learning.
- Self-Securing: Automatically upgrades and patches itself while running. Automatically applies security updates while running to protect against cyberattacks.
- Self-Repairing: Provides automated protection from downtime. SLA guarantees 99.995 percent reliability and availability, which reduces costly planned and unplanned downtime to less than 30 minutes a year
The Oracle Autonomous Database Cloud is revolutionizing how data is managed and analyzed, enabling faster, easier data access, helping to unlock the potential of your data so you can transform your business with innovation.
Magic Quadrant for Data Management Solutions for Analytics
- Adam M. Ronthal | Roxane Edjlali | Rick Greenwald
- 13 February 2018
The data management solutions for analytics market is evolving as the cloud's position solidifies, use cases for Hadoop clarify, logical data warehouse adoption grows, and Chinese vendors expand abroad. Against this dynamic backdrop, this report will help you find the right vendor for your business.
Market Definition/Description
We define a data management solution for analytics (DMSA) as a complete software system that supports and manages data in one or more file management systems (usually databases). DMSAs include specific optimizations to support analytical processing. This includes, but is not limited to, support for relational processing, nonrelational processing (such as graph processing), and machine learning and programming languages such as Python and R. Data is not necessarily stored in a relational structure, and multiple models can be used — for example, relational, XML, JSON, key-value, text, graph and geospatial.
Although the traditional data warehousing use case remains foundational to most organizations' analytics initiatives, there is also interest in the ability to manage and process increasingly diverse formats for both internal and external data. A complete DMSA must therefore be able to accommodate a diverse range of data types. These may include interaction and observational data — from Internet of Things (IoT) sensors, for example — as well as nonrelational data, such as text, image, audio and video data.
The breadth and scope of associated roles and skills is also expanding as organizations engage with new use cases that deliver a fuller understanding of data from an increasing number of sources.
We define four primary use cases for DMSAs that reflect this diversity of data and use cases (see also Note 1):
- Traditional data warehouse
- Real-time data warehouse
- Context-independent data warehouse
- Logical data warehouse (LDW)
Our definition also states that:
- A DMSA is not a specific class or type of technology.
- A DMSA may consist of many different technologies in combination. However, any offering or combination of offerings must, at its core, be able to provide access to data under management by open-access tools via standard APIs like Open Database Connectivity (ODBC), Java Database Connectivity (JDBC), representational state transfer (REST) and Object Linking and Embedding Database (OLEDB). Read more
Oracle Content
The Future of Data Management is Autonomous
The rise of data as a valuable asset of your organization, and therefore your enterprise database, will have a direct impact on the future success of your business. In this video, Vice President of Oracle Cloud Platform Product Marketing, Monica Kumar, discusses how Autonomous Database Cloud can transform your business and give you a competitive advantage.
White Paper: Oracle Autonomous Database
Building on the next generation of the industry leading Database 18c, the Oracle Autonomous Database Cloud integrates applied machine learning to deliver self-driving, self-securing, self-repairing administration. Oracle Autonomous Database eliminates human labor, human error, and manual tuning to enable unprecedented availability, high performance, security at much lower cost. Oracle Autonomous Database reduces downtime, both planned and unplanned, to less than 2.5 minutes a month total.
Read the white paper to understand its strategy and roadmap.
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