Database level consolidation
All of us DBA’s know how cumbersome and time-consuming it is to guesstimate the optimal distribution of different kinds of databases over the new instances while trying to consolidate DBMS workloads on a database level. In addition to the external dependency mapping challenges like linked servers, services, and applications, there is a need to understand databases’ workload profiles and how they behave seasonally and in long term.
Our software is a game-changer. Instead of a DBA’s consuming their time trying to script complex, manual performance data collection scripts and trying to analyze workloads in Excel, SQL Governor V14 supports database capacity planning and consolidation in a precise and comprehensive way.
- Create a database planning project by inputting some basic planning data like monitoring time span, planning scope, and whether to include or exclude service times and service breaks into the prediction model.
- Filter out and select those source servers’ databases you want to move into new SQL Server instance(s). You can filter the databases by business criticality, service, and workload characteristics.
- Select desired target server template(s) from various options (physical server, virtual machine, hyperconverged, Azure, or AWS setup) with the desired configuration.
- Create the most optimal and harmonized SQL Server database consolidation model by reviewing how the selected databases behave together with system databases against the target setup based on given performance threshold constraints such as CPU, RAM, and storage.
Monitoring Early Warnings
Another unique feature in SQL Governor V14 is our international patent pending Early Warning mechanism for SQL Server monitoring. It gives a DBA an insight to identify potential SQL Server performance counters on threats before something unwanted happens enabling you to start investigating the root cause before any potential problems occur. Innovation is based on a statistical time series analysis of the behavioral characteristics of the workload by dividing it into several risk-prone components and following their deviation from the performance counter baselines over time.
Jani K. Savolainen
Founder & CTO,