Key Methods for Managing Complex Database Environments

This white paper addresses key methods for successfully managing today’s complex database infrastructures, including balancing key business metrics, understanding the challenges DBAs face, and finding the right tools to monitor and manage the database environment.

A slow relational database can substantially impact the performance of the applications it supports. Users may issue thousands of transactions every minute, which are serviced by perhaps dozens of redundant web and application servers – and a single database. The relational database must preserve consistency and availability, making it a highly centralized asset. It concentrates the transactions and places a great deal of pressure on the database stack to operate at optimal levels of performance and availability. This is why the database is so critical, and it’s also why the DBAs who manage it are more than average administrators.

To successfully manage these complex database environments, one must balance key business metrics, understand the DBA’s unique challenges, and select the right tools for database administration. We value your time - here is a snippet of the content you will receive inside of this white paper.  

Key components of a monitoring technology that improves TCO include:

  • Centralized Architecture
    • Minimizes deployment and upgrade costs.
    • Offloads management, storage, and presentation overhead away from production.
    • Facilitates cross-instance, crossplatform, and cross-domain correlation of data and analyzes performance data.
  • Production-Remote Collection
    • Reduces the collection cost to the overhead of the collection query only.
    • Facilitates deployment and speeds upgrades by avoiding the need to touch the production server.
  • Database Auto-Discovery
    • Enables the monitor to be effective quickly in large database environments, removing the burden of the DBA team to individually specify each instance.
  • Adaptive Baseline Alerting
    • Leverages historical performance to construct a baseline range of “normal” performance for each collected metric.
    • Addresses the inability of fixed thresholds to provide accurate warning alerts, allowing them to remain focused on protecting critical resource limits.
    • Reports emerging problems as deviations from normal behavior with great accuracy and timeliness.
  • Service Level Modeling
    • Aligning groups of assets to a defined service level is an essential capability for a monitor designed to measure and report on the qualitative aspects of performance.
  • Consistent, Cross-Platform User Interface
    • Reduces training costs.
    • Accelerates time to resolution for DBAs of varying skill levels.
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