Breaking Out of the Proprietary Cage - Real-time Data Warehouses in Open Source
Offered By: Linux Foundation via YouTube
Course Description
Overview
Syllabus
Intro
Presenter Bio
What makes analytic applications special?
SQL data warehouses run analytic queries
What ClickHouse is not
Merge Tree is the workhorse table engine
Merge Tree data layout
Detailed storage layout within a single part /var/lib/clickhouse/data/airline/ontime
Adding CPUs boosts parallelized execution
I/O drives ClickHouse performance
Compression and codecs reduce I/O
Effect on storage is dramatic
Materialized views restructure/reduce data
Pattern: TTLs + downsampled views
Alternative pattern: Tiered storage
More table engines for clustering!
How do distributed queries work?
Pattern: Kafka-based ingestion pipelines
Alternative ingest pattern: Kafka engine
Pattern: Grafana visualization
Pattern: Operation on Kubernetes
Wrap-up . ClickHouse meets/beats proprietary SQL data warehouses in head-to-head comparisons
Taught by
Linux Foundation
Tags
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