Pathway – Build Mission Critical ETL and RAG in Python (NATO, F1 Used)
Hi HN data folks, I am excited to share Pathway, a Python data processing framework we built for ETL and RAG pipelines. https://github.com/pathwaycom/pathway We started Pathway to solve event processing for IoT and geospatial indexing. Think freight train operations in unmapped depots bringing key merchandise from China to Europe. This was not something we could use Flink or Elastic for. Then we added more connectors for streaming ETL (Kafka, Postgres CDC…), data indexing (yay vectors!), and LLM wrappers for RAG. Today Pathway provides a data indexing layer for live data…
Pathway – Build Mission Critical ETL and RAG in Python (NATO, F1 Used) launched on June 13, 2024 with 73 votes, #671 of 4,672 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Pathway is a Python data processing framework for building ETL and RAG pipelines. It handles streaming data processing from sources like Kafka and Postgres, performs stateless and stateful transformations, and enables vector-based data indexing for retrieval-augmented generation. Built with a Rust runtime using Differential Dataflow for incremental computation, Pathway is designed for data engineers and developers who need to process live data updates and maintain in-memory pipelines at scale.
written from the facts on this page · September 2026
From the sources
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. - pathwaycom/pathway
In the maker’s words, at launch
Hi HN data folks, I am excited to share Pathway, a Python data processing framework we built for ETL and RAG pipelines. https://github.com/pathwaycom/pathway We started Pathway to solve event processing for IoT and geospatial indexing. Think freight train operations in unmapped depots bringing key…
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GlassFlow – OSS streaming dedup and joins from Kafka to ClickHouse2025 · github.com · ▲78Hi HN! We are Ashish and Armend, founders of GlassFlow. We just launched our open-source streaming ETL that deduplicates and joins Kafka streams before ingesting them to ClickHouse https://github.com/glassflow/clickhouse-etl Why we built this: Dedup with batch data is straightforward. You load the data into a temporary table. Then, find only the latest versions of the record through hashes or keys and keep them. After that, move the clean data into your main table. But have you tried this with streaming data? Users of our prev product were running real-time analytics…
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