What does cloud native mean, and what are some design considerations when implementing cloud-native data services? Gwen Shapira (Apache Kafka® Committer and Principal Engineer II, Confluent) addresses these questions in today’s episode. She shares her learnings by discussing a series of technical papers published by her team, which explains what they’ve done to expand Kafka’s cloud-native capabilities on Confluent Cloud. 

Gwen leads the Cloud-Native Kafka team, which focuses on developing new features to evolve Kafka to its next stage as a fully managed cloud data platform. Turning Kafka into a self-service platform is not entirely straightforward, however, Kafka’s early day investment in elasticity, scalability, and multi-tenancy to run at a company-wide scale served as the North Star in taking Kafka to its next stage—a fully managed cloud service where users will just need to send us their workloads and everything else will magically work. Through examining modern cloud-native data services, such as Aurora, Amazon S3, Snowflake, Amazon DynamoDB, and BigQuery, there are seven capabilities that you can expect to see in modern cloud data systems, including: 

1. Elasticity: Adapt to workload changes to scale up and down with a click or APIs—cloud-native Kafka omits the requirement to install REST Proxy for using Kafka APIs
2. Infinite scale: Kafka has the ability to elastic scale with a behind-the-scene process for capacity planning 
3. Resiliency: Ensures high availability to minimize downtown and disaster recovery 
4. Multi-tenancy: Cloud-native infrastructure needs to have isolations—data, namespaces, and performance, which Kafka is designed to support
5. Pay per use: Pay for resources based on usage
6. Cost-effectiveness: Cloud deployment has notably lower costs than self-managed services, which also decreases adoption time 
7. Global: Connect to Kafka from around the globe and consume data locally

Building around these key requirements, a fully managed Kafka as a service provides an enhanced user experience that is scalable and flexible with reduced infrastructure management costs. Based on their experience building cloud-native Kafka, Gwen and her team published a four-part thesis that shares insights on user expectations for modern cloud data services as well as technical implementation considerations to help you develop your own cloud-native data system. 

EPISODE LINKS

• Cloud-Native Apache Kafka
• Design Considerations for Cloud-Native Data Systems
• Software Engineer, Cloud Native Kafka
• Join the Confluent Community
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer

SEASON 2
Hosted by Tim Berglund, Adi Polak and Viktor Gamov
Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed
Music by Coastal Kites
Artwork by Phil Vo

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