Enterprise Data Maps for Finops
We are using a new event-based approach to data management which discovers the current status in data ecosystems. Metadata is collected from heterogeneous data processing and storage systems, distributed through streaming and combined and analyzed via specific data enrichment processes. As most of the value in the metadata is gained through understanding the complex relationships between entities, these are stored as a graph that allows the entire enterprise data to be mapped, such that questions about where data is stored, how are those storage system protected, where does data flow, can be answered at the level of the enterprise rather than a single isolated processing system. We call this tool PathFinder.
Read more about Pathfinder here.
In this context, we would like to explore the use of enterprise data maps for FinOps use cases.
Our research objectives include:
- Link aggregated billing information to resources at a fine grained level (e.g. estimate the cost of a running application in a OpenShift cluster given the total cluster cost) and enrich the data graph.
- Gather data about deployment cost of resources across different providers (e.g. what is the cost of running a PostgresSQL instance on premise vs a managed instance on AWS) taking into account bandwidth cost for the communication between cross cloud deployments.
- Analyze the graph to highlight "expensive" regions.
- Run graph optimization algorithms to suggest alternative deployment architectures (e.g. eliminating intermediary nodes or migrating to a different data store provider).
- Associate users with costs traversing the graph edges and nodes.
- Build extensions to the Pathfinder UI to enable FinOps visualizations.
Duration of the internship: 6 months
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
How to apply
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