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Harness the Autonomous Data Warehouse--the best Oracle data warehouse solution!
December 08, 2020

Harness the Autonomous Data Warehouse--the best Oracle data warehouse solution!

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is used in automation of provisioning and configuring a data warehouse. It is also used to tune and scale the data warehouse as needed. It is used across the whole organization.
Business problems that it addresses: Since the manual work is almost completely eliminated, the cost of administering a data warehouse is reduced significantly. Also, this is highly secured and reliable.
  • High performance using continuous query optimization, table indexing, data summaries, and auto-tuning
  • Autonomous data encryption and security patch application
  • Different deployment models--shared, dedicated, and cloud@customer
  • Built in analytics--this makes data loading, indexing, and building good data visualization models easier
  • Improved machine learning capabilities
  • I find it to be the best autonomous solution out there with high scalability and reliability
  • More capabilities of Analytics Cloud
  • Overall positive impact--Significantly reduced admin cost
  • Auto-tuning, patching, and indexing reduced manual intervention
  • Built-in reports eliminated the integration of a data warehouse with other reporting tools
Well suited when
1. High performance is needed--The autonomous data warehouse is capable of increasing performance for continuous query optimization, table indexing, data summaries, and auto-tuning even as data volume and number of users grows.
2. High scalability is needed--Unlike other cloud services that require downtime to scale, Oracle Autonomous Data Warehouse scales while the service continues to run.
3. Automation is needed--Oracle Autonomous Data Warehouse automates provisioning, configuring, securing, tuning, and scaling for a data warehouse.

Less suited when
1. There is not a significant amount of data that needs to be handled on a daily basis.
2. Data analytics is not a requirement.