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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Segment provides the reliable data infrastructure companies need to easily collect, clean, and control their customer data. Once you try it, you’ll understand why Segment is one of the hottest companies coming out of Silicon Valley. Segment recently launched a Startup Program so that early-stage startups can get a Segment account totally free up to $25k, plus exclusive deals from some favorite vendors and other resources to become data experts. Go to dataengineeringpodcast.com/segmentio today and see if you or a startup you know qualify for the program today.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
- Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
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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