One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale with your business.
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Alluxio provides an open source unified data orchestration layer for hybrid and multi-cloud environments, making data accessible wherever data computation and processing is done. By seamlessly pulling data from underlying data silos, Alluxio unlocks the value of data and allows for modern data-intensive workloads to become truly elastic and flexible for the cloud.
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- Your host is Tobias Macey and today I’m interviewing Evan Weaver about FaunaDB, a modern operational data platform built for your cloud
- How did you get involved in the area of data management?
- Can you start by explaining what FaunaDB is and how it got started?
- What are some of the main use cases that FaunaDB is targeting?
- How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?
- Can you describe the architecture of FaunaDB and how it has evolved?
- The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?
- What are some of the edge cases that users should be aware of?
- How are conflicts managed in Fauna?
- What is the underlying storage layer?
- How is the query layer designed to allow for different query patterns and model representations?
- How does data modeling in Fauna compare to that of relational or document databases?
- Can you describe the query format?
- What are some of the common difficulties or points of confusion around interacting with data in Fauna?
- What are some application design patterns that are enabled by using Fauna as the storage layer?
- Given the ability to replicate globally, how do you mitigate latency when interacting with the database?
- What are some of the most interesting or unexpected ways that you have seen Fauna used?
- When is it the wrong choice?
- What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company?
- What do you have in store for the future of Fauna?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Ruby on Rails
- Spanner Paper
- DynamoDB Paper
- Calvin Protocol
- Daniel Abadi
- LSM Tree (Log-structured Merge-tree)
- Change Data Capture
- Fauna Query Language (FQL)
- CQL == Cassandra Query Language
- Object-Relational Databases
- LDAP == Lightweight Directory Access Protocol
- OLAP == Online Analytical Processing
- Jepsen distributed systems safety research