Building high-throughput, scalable embeddings indexing pipelines can be simplified using Redpanda and Neon, as demonstrated in a blog post that details a no-code setup for this process. By leveraging Redpanda for data ingestion and Neon for efficient indexing within a managed environment, users can create robust pipelines suitable for tasks such as indexing e-commerce transactions. Redpanda's compatibility with the Apache Kafka protocol facilitates smooth data handling, while Neon's serverless Postgres capabilities with pgvector extension ensure efficient storage and querying of data embeddings. The blog outlines a practical example where invoice data from an e-commerce application is processed, with Redpanda acting as an intermediate buffer and Ollama used for computing embeddings, before the data is stored in Neon for querying. This approach offers a streamlined solution for building scalable and high-performing indexing pipelines without the need for extensive coding, making it accessible for various data processing projects.