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Autonomous Data Warehouse Use Case Patterns

Autonomous Data Warehouse Use Case Patterns

Oracle Autonomous Data Warehouse is Oracle's new, fully managed database tuned and optimized for data warehouse workloads with the market-leading performance of Oracle Database. It delivers a completely new, comprehensive cloud experience for data warehousing that is easy, fast, and elastic.

Below is information about the four most common use case patterns for Autonomous Data Warehouse - from moving your existing data warehouse to the cloud to building data discovery sandboxes.

What's the different between a data warehouse, data mart and a data lake?
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Data Warehouse Reference Architecture Patterns

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Departmental Marts

A departmental mart is used by individual teams or individual departments or groups and is intentionally limited in scope because it focuses on a clearly defined subset of data usually managed within a spreadsheet package.

Autonomous Database makes it easy to move departmental marts to a safe, secure always-on cloud...

Departmental Mart From E-Business Suite

Analysts need an efficient way to consolidate data from multiple financial systems and other data sources into a trusted, maintainable, and query-optimized source.

Typically data mart projects struggle to optimize data from multiple sources which makes it very difficult to effectively analyze the data and generate actionable insights.

Autonomous Data Warehouse makes it easy to resolve these key challenges...

Departmental Mart For Human Capital

Human resource data is often distributed in multiple systems across the enterprise and can't easily be integrated and analyzed to produce actionable insights.

This makes it very difficult to capture raw application data, enrich it with data from other sources and produce actionable and predictive insights.

Autonomous Data Warehouse makes it easy to resolve these key challenges......

Enterprise Data Warehouse with Data Lakes

A modern data warehouse collects data from a wide variety of sources, both internal or external. The data is usually structured, often from relational databases, but it can be unstructured too pulled from "big data" sources such as Internet of Things devices etc.


Autonomous Data Warehouse makes it easy to integrate and secure data from many sources and then generate richer, smarter, faster business insights...




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You can find more reference architectures patterns for Autonomous Data Warehouse...
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Data Warehouse Solution Patterns

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Migrating to Autonomous Data Warehouse

This architecture shows the resources and topology required to migrate an on-premises deployment of Oracle Database Enterprise Edition to an Oracle Autonomous Transaction Processing database provisioned on shared Exadata infrastructure in Oracle Cloud.

ADW With Private Endpoints

This architecture uses an autonomous database (which can be an Oracle Autonomous Transaction Processing database or an Oracle Autonomous Data Warehouse) provisioned in a private subnet; that is with a private endpoint.

Building a Data Science Environment

This is a flexible architecture that can support multiple scenarios based on Oracle Machine Learning in Autonomous Data Warehouse. In addition to Autonomous Data Warehouse, it includes Data Catalog and Oracle Analytics Cloud along with three Oracle Cloud Infrastructure Compute instances.

Automated Data Loading in the Cloud

This reference architecture shows how you can use a serverless function to automate the process of extracting data from files generated by various databases or applications and loading the data into a data warehouse for analysis.




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You can find more solution playbooks for Autonomous Data Warehouse...
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Oracle Big Data Warehousing Blog