IMPACT 2019: The motivations and benefits of building a home-grown capacity management system – Len Wyatt, Blackbaud, Inc. – Computer Measurement Group

IMPACT 2019: The motivations and benefits of building a home-grown capacity management system – Len Wyatt, Blackbaud, Inc.

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IMPACT 2019: The motivations and benefits of building a home-grown capacity management system – Len Wyatt, Blackbaud, Inc.

The motivations and benefits of building a home-grown capacity management system - Len Wyatt, Blackbaud, Inc.

There are commercial capacity management tools out there, so why would anyone build their own? For Blackbaud, there were both technical and organizational reasons. There have been some surprising benefits. This talk surveys the motivations, the architecture, the benefits and the tradeoffs of creating your own Capacity Management Data Warehouse (CMDW).

The primary uses for CMDW include monitoring current systems for capacity concerns, trying to anticipate upcoming needs, forecasting resource needs as we move from physical servers to a virtualized data center and to a cloud-based infrastructure, and troubleshooting issues based on data that was not visible before. On the monitoring front, as the number of systems monitored has grown, we have moved toward a process of looking for statistical anomalies in the data and having people investigate data that was first uncovered by the statistics. Forecasting is still a spreadsheet-driven process, but the CMDW provides data that lets us project from current environments to future configurations.

This talk will outline the variety of data sources that feed into CMDW, dive into how a traditional data warehouse architecture using a relational database has worked as the core mechanism, and where we extended the concepts of a relational warehouse for synchronizing disparate data sources and for doing statistical analysis. A look at the varied uses of CMDW comes next: both the things we planned to do and the things that popped up once people saw the ability to collect and analyze data in new ways. At the end, we’ll “fess up” to some of the issues that came up as well.

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