Database indexes are critical to ensure fast lookups of your data, but they are inherently tied to the database engine. Pilosa is rewriting that equation by providing a flexible, scalable, performant engine for building an index of your data to enable high-speed aggregate analysis. In this episode Seebs explains how Pilosa fits in the broader data landscape, how it is architected, and how you can start using it for your own analysis. This was an interesting exploration of a different way to look at what a database can be.
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- Your host is Tobias Macey and today I’m interviewing Seebs about Pilosa, an open source, distributed bitmap index
- How did you get involved in the area of data management?
- Can you start by describing what Pilosa is and how the project got started?
- Where does Pilosa fit into the overall data ecosystem and how does it integrate into an existing stack?
- What types of use cases is Pilosa uniquely well suited for?
- The Pilosa data model is fairly unique. Can you talk through how it is represented and implemented?
- What are some approaches to modeling data that might be coming from a relational database or some structured flat files?
- How do you handle highly dimensional data?
- What are some of the decisions that need to be made early in the modeling process which could have ramifications later on in the lifecycle of the project?
- What are the scaling factors of Pilosa?
- What are some of the most interesting/challenging/unexpected lessons that you have learned in the process of building Pilosa?
- What is in store for the future of Pilosa?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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