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GlassFlow: ClickHouse Duplications and JOINs solved for Kafka Users
Learn how we will solve the biggest challenges of Kafka users with ClickHouse.

GlassFlow: ClickHouse Duplications and JOINs solved for Kafka Users
Learn how we will solve the biggest challenges of Kafka users with ClickHouse.
Part 1: How do you usually ingest data from Kafka to ClickHouse?
Deep dive on raw data ingestions from Kafka to ClickHouse.
Part 2: Why are duplicates happening and JOINs slowing ClickHouse?
Learn the root of the duplication and JOINs issues of Kafka to ClickHouse.
Part 3: ClickHouse ReplacingMergeTree and Materialized Views are not enough
Deep dive on limitations of ReplacingMergeTree and Materialized Views.
Part 4: Can Apache Flink be the solution?
Apache Flink isn't the solution for duplications and JOINs on ClickHouse.
Part 5: How GlassFlow will solve Duplications and JOINs for ClickHouse
Learn the details on how GlassFlow will solve Duplications and JOINs.
ClickHouse Deduplication with ReplacingMergeTree: How It Works and Limitations
ReplacingMergeTree deduplication in ClickHouse – and its limitations.
Clickhouse and Its Limitations with JOINS
Clickhouse and the limitations when it comes to JOINS
How to Solve JOIN Limitations in ClickHouse
Struggling with JOINs in ClickHouse? Learn how to handle them efficiently.
Cleaned Kafka Streams for ClickHouse
Clean Data. No maintenance. Less load for ClickHouse.
GitHub Repo