Gwen Shapira, founder of Nile and a database expert with a background at Confluent, dives into the complexities of multi-tenant databases. She discusses the challenges of implementing Distributed DDL at scale and the transition from big tech to startup life. Gwen touches on AI's role in project management and the balance between human intuition and automation. The conversation highlights the advantages of Postgres over NoSQL, the importance of data isolation in AI applications, and the synergy between LLMs and traditional machine learning techniques.
Gwen Shapira emphasizes the challenges of scaling multi-tenant databases while ensuring data consistency and localized performance for diverse tenants.
Shapira highlights the importance of innovative storage solutions, combining relational and vector databases to enhance operational efficiency in AI applications.
Deep dives
Gwen Shapira's Background and Experience
Gwen Shapira has extensive experience in database technology, having spent over two decades in the field. She founded Nile, where she focuses on developing multi-tenant databases with a fresh perspective on how they can empower applications. Prior to Nile, Gwen contributed significantly to Confluent in various roles associated with Apache Kafka, and her past experiences include working on Hadoop and Oracle databases. Her deep knowledge and hands-on approach in the operational database space have established her as a thought leader in the industry.
Innovative Approaches to Distributed Databases
Nile's approach aims to facilitate a distributed database that can effectively scale across multiple regions while delivering localized performance for diverse tenants. Shapira emphasizes the importance of providing each tenant their own computing resources and ensuring data consistency across geographically dispersed systems. The technology focuses on executing database commands such as 'create table' uniformly for all tenants, regardless of their location, through a sophisticated implementation of transaction coordination. This not only enhances performance but also addresses challenges associated with multi-tenant architectures.
Identifying Market Needs and Evolution of Solutions
The journey of establishing Nile began three years ago when Shapira and her co-founder aimed to create a simplified framework for Software as a Service (SaaS) applications. Through extensive interaction with companies, they discovered a significant gap in the market regarding multi-tenant database solutions, which led them to pivot their focus to address these specific customer needs. Their shift came after realizing that many existing database solutions had not evolved to adequately support the complexities of modern application architectures. This highlighted the necessity for a more integrated database solution that inherently understands multi-tenant structures and customer requirements.
Future Trends in Database Technology
Looking ahead, Shapira expresses enthusiasm about the evolving landscape of database technology, especially concerning the processing of data at the edge, enabling a more seamless integration with local applications. She envisions a future where powerful computing capabilities are utilized more effectively, leveraging both cloud and local resources. This is particularly relevant as AI applications continue to proliferate, necessitating innovative storage solutions such as Nile's vector database approach. As organizations seek to accommodate various data types and optimize performance, the combination of relational and vector databases presents a promising avenue for enhancing operational efficiency.
Multi-tenancy in databases is very difficult to pull off at scale. Gwen Shapira and I chat about multi-tenant databases at Nile (and elsewhere), AI, RAG, and much more.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode