Oracle Autonomous Data Warehouse (ADW)–What and Why?

Oracle’s Cloud Infrastructure (OCI) introduced the world to Autonomous Databases and a more automated way of thinking about managing your data. In this first-of-a-kind Autonomous database, Oracle has taken a pioneering leap into the future of database management.  Through its pioneering ways, Oracle is bringing the next evolution of database technology to customers. Through its Autonomous Database Services, Oracle is enabling businesses to plug and play database technology without having to personally install, configure, secure, run, and manage. These autonomous services are the keys behind Oracle Cloud Infrastructure (OCI); delivering value propositions that are absent in other cloud providers’ offerings.

Oracle Autonomous Date Warehouse (Oracle ADW) delivers:

• Flexible data ingestion and analysis of a wide variety of data types
• Security built in with advanced intrusion detection and encryption
• Reduce cost of administration up to 90% compared to other data warehouses
• Continuous data integration with standard tool sets

Related reading by RheoData:

Terraforming your way to Oracle Autonomous Databases

What is Oracle Autonomous Data Warehouse?

Autonomous Data Warehouse is a fully automated cloud database service optimized for analytic workloads, including data marts, data warehouses, and data lakes. It is preconfigured with columnar format, partitioning, and large joins to simplify and accelerate database provisioning, extracting, loading, and transforming data; running sophisticated reports; generating predictions; and creating machine learning models. With Autonomous Database, data scientists, business analysts, and nonexperts can rapidly, easily, and cost-effectively discover business insights using data of any size and type – deliverable in both the Oracle Cloud Infrastructure or Oracle Cloud@Clustomer behind a customer’s firewall.

Key Values of the Autonomous Database

• Autonomous Management: Eliminate nearly all manual and complex tasks that cost money, take time, and sometimes lead to error.
• Top Performance: All system aspects are continuously monitored for optimal efficiency with adjustments made in the background depending on workloads, query type, and the number of users.
• Big Data Enablement: Accelerate analytics and data insight extraction.
• Instant Elasticity: Preconfigured compute and storage shapes can independently scale up and down, without any downtime.
• Enterprise-Grade Security: Data encryption by default (both in transit and at rest) and self-upgrades of security patches.

Key Use Cases

• Enterprise Data Warehouse
• Data Lakes
• Departmental Data Warehouse
• Data Science and Machine Learning
• Data and IoT event streams