Intro to ClickHouse - part II by Tinybird: engines, query performance and using materialized views

Tinybird is a product that lets developers and data engineers ingest, transform and create real-time APIs on billions of rows of data very easily. It uses ClickHouse underneath, and we strive to understand it deeply.

The audio is in Spanish

In this second guide, we go over some more advanced topics, like

00:00:00 Intro
00:01:48 Using a Log table to insert a 23M-row CSV file into ClickHouse (with a Log table)
00:09:28 Using a MergeTree table to insert a 23M-row CSV file into ClickHouse
00:15:28 Query performance depending on the table engine used (Log VS MergeTree in this case)
00:20:32 Filtering by columns used as sorting keys VS other columns
00:25:00 Intro to Materialized views - one of the most powerful features of ClickHouse
00:26:55 Creating Materialized views on ClickHouse. Differences with materialized views on PostgreSQL
00:34:48 Performance improvements of using materialized views
00:35:07 Materialized views are incremental on ClickHouse. Inserting new data that affects materialized views.
00:47:46 Memory implications of using materialized views
00:48:55 Q&A
00:49:55 Performance implications of column types and nullables. Use the lowest precision you can to improve speed

[whitestork]

Why would you give an English title to a Spanish video? Very confusing