Writing to Kafka allowed them to continue their current ingestion process into MariaDB at the same time as ClickHouse. Kafka consumer groups allow the data to be consumed twice by different consumer pools that have different throughput without introducing bottlenecks.
From experience the Kafka tables in ClickHouse are not stable at a high volumes, and harder to debug when things go sideways. It is also easier to mutate your data before ingestion using Vector's VRL scripting language vs. ClickHouse table views (SQL) when dealing with complex data that needs to be denormalized into a flat table.
That seems weird... is it too hard to feed everything into an Elasticsearch index? Even with default settings and some decent hardware you'd get good stemming and relevance ranking pretty easily.
From experience the Kafka tables in ClickHouse are not stable at a high volumes, and harder to debug when things go sideways. It is also easier to mutate your data before ingestion using Vector's VRL scripting language vs. ClickHouse table views (SQL) when dealing with complex data that needs to be denormalized into a flat table.