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Software Status:Discontinued

About OtterTune

OtterTune is an automated database tuning service from Carnegie Mellon University that aimed to improve database performance. It provided features such as automatic parameter tuning, performance monitoring, and workload analysis so users could achieve more efficient database operations. The service allowed users to improve their database systems without extensive manual intervention. However, as of 2024, OtterTune is no longer operational. Despite its modern approach to database management, this service has been discontinued, leaving users without access to its key functionalities. Key capabilities: automatic parameter tuning performance monitoring workload analysis Best for: database administrators that need automated tuning solutions for database improvement.

OtterTune Details

Vendor
Carnegie Mellon University
Year Launched
Location
5000 Forbes Ave, Pittsburgh, PA 15213
Deployment
Training Options
demo, account manager, community
Countries Served
All Countries.
Languages
English, Spanish, French, German, Chinese, Japanese, Korean, Russian, Portuguese, Italian, Dutch, Arabic, Hindi, Turkish, Polish, Swedish, Danish, Norwegian, Finnish, Greek, Hebrew, Czech, Hungarian, Romanian, Slovak, Thai, Indonesian, Malay, Vietnamese, Tagalog, Swahili, Bengali, Urdu
Users
Database Administrators, Data Scientists, Developers, IT Managers
Industries Served
Healthcare, Education, Finance, Retail
Tags
Database Monitoring, Performance Tuning, Machine Learning, Automated Optimization, Carnegie Mellon University

OtterTune's In-App Market Place

Does OtterTune have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

1

Mini Apps

N/A

Pricing Options

Free trial
Free version
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Accepted Payment Currencies

USD ($), EUR (€), GBP (£), JPY (¥), AUD (A$), CAD (C$), CHF (CHF), CNY (¥), SEK (kr), KRW (₩), INR (₹), RUB (₽), BRL (R$), MXN (Mex$), SGD (S$), PLN (zł), TRY (₺), NZD (NZ$), HKD (HK$), NOK (kr), ZAR (R)

Pros & Cons

  • Utilizes machine learning algorithms to automatically optimize database performance
  • Improves query performance and reduces latency
  • Provides recommendations for database tuning based on real-time monitoring
  • Saves time and effort by automating the tuning process
  • Helps in identifying and fixing performance issues quickly and efficiently
  • Currently only supports MySQL and PostgreSQL databases
  • Limited customization options for tuning recommendations
  • Requires a high level of technical expertise to implement and interpret recommendations
  • Not suitable for small-scale databases or non-enterprise environments

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