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Learn how to build elastic AI data pipelines that overcome traditional scaling bottlenecks in this 30-minute conference talk. Discover why conventional event-driven architectures using Kafka and MongoDB face critical limitations when traffic spikes occur, including partition rebalancing delays and costly database shard rebalancing. Explore Apache Pulsar's decoupled compute-from-storage architecture that enables seamless scaling without rebalancing partitions, and understand how EloqDoc, a MongoDB-compatible elastic document store, follows the same decoupled principles to deliver 1000x faster compute scaling and distributed buffer pool expansion. Master the advantages of combining Pulsar with EloqDoc as a superior alternative to Kafka and MongoDB for AI applications that require true elasticity and cost-efficiency during unpredictable workload spikes. Examine how sink connectors propagate data to multiple downstream databases including MongoDB, vector databases, and Elasticsearch, while learning about ConvertDB's unified interface that simplifies data management through multiple APIs and cross-model transaction support. Gain insights into knowledge-based agents in GenTech applications and understand how converged databases can simplify complex multi-modal data interactions essential for RAG pipelines and knowledge graphs.
