Oracle Cloud Infrastructure (OCI) Ops Insights, or OPSI, is a powerful monitoring and observability platform designed to enhance the management and performance of various Oracle technologies, including Exadata systems, databases, and hosts. The recent suite of updates introduced by Oracle is a testament to their commitment to innovation in the cloud computing space. These enhancements cater to the unique requirements of DevOps professionals, IT operations teams, Database Administrators (DBAs), and developers, enabling them to excel in the Oracle Cloud Infrastructure (OCI) environment. This blog provides a visual and informative overview of the key features introduced in the upcoming Observability Insights session promoted below.
GPU Dashboard:
Data Explorer and Dashboard Enhancements:
Ops Insights in Database Management (OPSI in DBM):
- OCI Ops Insights now supports MySQL, including HeatWave, an in-memory database engine.
- Enables monitoring and optimization of MySQL performance for high-performance applications.
The webinar not only showcases these immediate enhancements but also shares developments, promising to revolutionize Exadata and database management in OCI. These upcoming features aim to provide unparalleled operational efficiency, especially in hybrid environments. By attending the webinar, professionals can:
In conclusion, the latest OCI Ops Insights updates offer a comprehensive toolkit to enhance the productivity of various technical roles. Through these visual representations, it is evident that Oracle is dedicated to providing an intuitive and powerful cloud management experience, empowering users to excel in the competitive cloud computing landscape.
Try our Live Labs now. Learn how to gain applied observability insight using the Oracle Cloud Infrastructure Ops Insights service. Understand how to use its long term capacity planning, SQL performance analysis, database and host resource utilization and capacity capabilities. See how Ops Insights helps you uncover performance issues, forecast consumption, and plan capacity using machine-learning based analytics on historical and SQL data. Then use it to make data-driven decisions to optimize resource use, proactively avoid outages, and improve performance.
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