My main use case for Estuary is to target replacing slow, batch-heavy ETL pipelines with millisecond latency change data capture pipelines for our primary real-time ETL streaming platform for our transactional databases. We use PostgreSQL, and we are using a streaming pipeline Kafka direct with our vector DB, Pinecone, and analytical data warehouse, Snowflake. This allows us to feed fresh transactional data and embeddings correctly into the downstream retrieval-augmented generation RAG, giving us real-time AI agents without writing custom pipeline code or maintaining heavy infrastructure.
Data Integration facilitates the combination of data from diverse sources into a unified view, crucial for businesses to make informed decisions and enhance operational efficiency. With comprehensive solutions available, organizations can streamline their data workflows. Data Integration solutions are vital for businesses aiming to handle large volumes of data efficiently. These solutions help in synchronizing data from multiple sources, ensuring consistent data across platforms, and...
My main use case for Estuary is to target replacing slow, batch-heavy ETL pipelines with millisecond latency change data capture pipelines for our primary real-time ETL streaming platform for our transactional databases. We use PostgreSQL, and we are using a streaming pipeline Kafka direct with our vector DB, Pinecone, and analytical data warehouse, Snowflake. This allows us to feed fresh transactional data and embeddings correctly into the downstream retrieval-augmented generation RAG, giving us real-time AI agents without writing custom pipeline code or maintaining heavy infrastructure.