GlassFlow – OSS streaming dedup and joins from Kafka to ClickHouse
Hi 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…
In plain words
GlassFlow is an open-source streaming ETL tool that deduplicates and joins Kafka streams before ingesting them into ClickHouse. It addresses the challenge of handling duplicate records in real-time data pipelines, which commonly occur when data flows from multiple sources like CRMs and clickstream systems. The tool is designed for data engineers and analytics teams building real-time analytics pipelines who need to ensure data quality and accuracy before loading into ClickHouse.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi 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 pipelines from Kafka to ClickHouse and noticed that the analyses were wrong due to duplicates. The source systems produced duplicates as they ingested similar user data from CRMs, shop systems and click streams. We wanted to solve this issue for them with the existing ClickHouse options, but ClickHouse ReplacingMergeTree has an uncontrollable background merging process. This means the new data is in the system, but you never know when they’ll finish the merging, and until then, your queries return incorrect results. We looked into using FINAL but haven't been happy with the speed for real-time workloads. We tried Flink, but there is too much overhead to manage Java Flink jobs, and a self-built solution would have put us in a position to set up and maintain state storage, possibly a very large one (number of unique keys), to keep track of whether we have already encountered a record. And if your dedupe service fails, you need to rehydrate that state before processing new records. That would have been too much maintenance for us. We decided to solve it by building a new product and are excited to share it with you. The key difference is that the streams are deduplicated before ingesting to ClickHouse. So, ClickHouse always has clean data and less load, eliminating the risk of wrong results. We want more people to benefit from it and decided to open-source it (Apache-2.0). Main components: - Streaming deduplication: You define the deduplication key and a time window (up to 7 days), and it handles the checks in real time to avoid duplicates before hitting ClickHouse. The state store is built in. - Temporal Stream Joins: You can join two Kafka streams on the fly with a few config inputs. You set the join key, choose a time window (up to 7 days), and you're good. - Built-in Kafka source connector: There is no need to build custom consumers or manage polling logic. Just point it at your Kafka cluster, and it auto-subscribes to the topics you define. Payloads are parsed as JSON by default, so you get structured data immediately. As underlying tech, we decided on NATS to make it lightweight and low-latency. - ClickHouse sink: Data gets pushed into ClickHouse through a native connector optimized for performance. You can tweak batch sizes and flush intervals to match your throughput needs. It handles retries automatically, so you don't lose data on transient failures. We'd love to hear your feedback and know if you solved it nicely with existing tools. Thanks for reading!
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