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As today's enterprises embrace big data, their information architectures are evolving. The new information architecture in the big data era embraces emerging technologies such as Hadoop, but at the same time leverages the core strengths of previous data warehouse architectures.
The data warehouse, built upon Oracle Database 18c and Exadata, will continue to be the primary analytic database for storing core transactional data: financial records, customer data, point- of-sale data and so forth (see Key Data Warehousing and Big Data Capabilities for more information).
However, the data warehouse will be augmented by a big-data system (built upon Oracle Big Data Appliance), which functions as a ‘data reservoir’. This will be the repository for the new sources of large volumes of data: machine-generated log files, social-media data, and videos and images -- as well as a repository for more granular transactional data or older transactional data which is not stored in the data warehouse.
Data flows between the big data system and the data warehouse to create a unified foundation: the Oracle Big Data Management System.
The transition from the Enterprise Data Warehouse centric architecture to the Big Data Management System - both on-premise, on the Cloud, or in hybrid Cloud systems - is going to revolutionize any companies information management architecture. Oracle's Statement of Direction outlines Oracle's vision for delivering innovative new technologies for building the information architecture of tomorrow.
Statement of Direction for Big Data and Data Warehouse Management Systems
Oracle Data Management Solutions: Leadership From The Data Center To The Cloud
Oracle has delivered four decades of data management and analytics innovation. Leading industry analysts have consistent pointed out how Oracle stands out from the competition by offering a truly unique value proposition to its customers, and by its ability to consistently and efficiently execute on its strategy.
Better, faster analytic queries - Database In-Memory delivers speed-of-thought processing for sophisticated analytic queries. Database In-Memory implements leading-edge columnar data processing to accelerate your data warehouse analytics by orders of magnitude. Answers that used to take minutes to obtain are now available instantly. Questions that you used to dream about are asking can now be quickly and easy answered.
Easier, faster management with new Multitenant enhancements - Oracle Multitenant is the architecture for the next-generation data warehouse in the cloud. It delivers easier consolidation of data marts and data warehouses by offering complete isolation, agility and economies of scale. Oracle Multitenant offers additional benefits by providing a fast and efficient management framework for delivering sandboxes and data discovery platforms within the overall Oracle Big Data Management System.
Maintaining consistent performance as data warehouse grows with Oracle Partitioning which enhances the manageability, performance, and availability of large data marts and data warehouses. It increases query performance by only working on the data that is relevant, improves availability through individual partition manageability and decreases costs by storing data in the most appropriate manner.
Temperature-based data optimization is made possible with Automatic Data Optimization which allows you to create policies for smart data compression and data movement making it possible to implement storage and compression tiering. ADO works in conjunction with Oracle Partitioning.
Effective use of all hardware resources - the Oracle Database is able to efficiently leverage all hardware resources - multiple CPUs, multiple IO channels, multiple storage units, multiple nodes in a cluster. The Oracle Optimizer can automatically decide if a query should run in parallel and the degree of parallelism to use based on the resource requirements of the statement.
Data Warehousing in the Oracle Cloud
Enterprise-proven data warehouse cloud services that support any size of data warehouse and big data workload from dev/test to large scale production deployment. Oracle Cloud delivers multi-layered, in depth security with encryption switched on by default. It's a highly available and scalable service delivering speed, simplicity and flexibility for faster time to value.
Oracle Autonomous Data Warehouse Cloud uses the same Oracle Database software and technology that runs your existing on-premises marts, data warehouses, and applications, making it compatible with all your existing data warehouse, data integration and BI tools.
Data Warehousing On-premise
Oracle Exadata Machine runs all types of data warehouse workloads including data marts, data warehousing, in-memory analytics and data mart consolidations. Simple and fast to implement, the Exadata Database Machine powers and protects your most important data sets and is the ideal foundation for a consolidated data warehouse cloud.
Query any data across Hadoop, NoSQL, and Oracle Database - Oracle Big Data SQL is a unique innovation from Oracle. It is a new architecture for SQL on Hadoop, seamlessly integrates data in Hadoop and NoSQL with data in Oracle Database. Oracle Big Data SQL radically simplifies integrating and operating in the big data domain.
Sophisticated SQL features for Big DW ecosystem
Built upon the very latest ANSI SQL standard (2016), Oracle's powerful and extensive library of SQL functions and features provides:
1. Faster analysis for interactive, iterative data exploration using Approximate Query Processing and pattern matching.
2. Seamless access to new types of data such as JSON data and XML.
3. Smarter views and simpler queries using Analytic Views which move business logic into database and simplifies SQL for complex queries.
4. Richer analytics with in-database graph analytics that discover relationships, recommendations and other key graph patterns.
5. Deeper analytics with Oracle's Advanced Analytics which delivers predictive analytics, data mining, text mining, statistical analysis, advanced numerical computations inside the database.
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