TL;DR

Building elastic AI data pipelines faces scaling challenges with traditional event-driven architectures. Apache Pulsar and EloqDoc offer a novel solution by decoupling compute from storage, enabling seamless scaling and faster compute. This combination provides true elasticity and cost-efficiency for workloads with unpredictable spikes.

Opening

Imagine a world where your AI application needs to seamlessly scale during unexpected traffic spikes without the hassle of rebalancing partitions or enduring costly delays. Traditional event-driven architectures, relying on Kafka and MongoDB, struggle with these demands, often becoming bottlenecks that hinder performance and increase costs. Enter Apache Pulsar and EloqDoc, a revolutionary pairing that redefines data streaming by decoupling compute from storage, offering a new level of elasticity and efficiency tailored for AI-driven workloads.

What You'll Learn (Key Takeaways)

• Decoupled Architecture Advantage – Learn how Apache Pulsar’s and EloqDoc’s architecture separates compute from storage, allowing for seamless scaling and improved performance without the need for rebalancing.
• Elasticity in Data Pipelines – Discover how the Pulsar + EloqDoc combination provides true elasticity, accommodating unpredictable workload spikes cost-effectively.
• Unified Interface with ConvertDB – Explore how ConvertDB simplifies data management by offering multiple APIs and supporting cross-model transactions, reducing operational complexity.

Q&A Highlights

Q: How do you think about knowledge-based agents in GenTech applications?
A: Data serves as the fuel for GenTech applications, driving rich datasets essential for RAG pipelines and knowledge graphs. Managing multiple data modes like SQL, Mongo, Redis, and Vector separately is challenging, but cross-model transactions, as enabled by converged databases, simplify these interactions.

The session provided insights into overcoming traditional scaling challenges with a modern, elastic approach using Apache Pulsar and EloqDoc, catering to the needs of AI-native applications.