Discover the hidden complexity layers of ClickHouse's distributed architecture and learn how to navigate the operational challenges that come with scaling analytical workloads. This video explains why ClickHouse requires specialized expertise and how to abstract away complexity for developers.

Learn how to design efficient ClickHouse schemas that maximize performance and minimize storage. This video covers key schema design principles, data type selection, sorting keys, codecs, and schema evolution, helping developers and data engineers build fast, scalable real-time analytics on ClickHouse.

Learn how ClickHouse table engines define data architecture, storage, and query behavior. This video covers the MergeTree family including standard MergeTree for append-only data, ReplacingMergeTree for deduplication, SummingMergeTree for numeric aggregation, and AggregatingMergeTree for complex pre-aggregation.

ClickHouse can query billions of records in milliseconds, but the real challenge is reliably ingesting those billions of rows. While benchmarks highlight query performance, production systems struggle with ingestion complexity. This video explains why ClickHouse ingestion is hard to scale.

This video covers the three main types of data ingestion into ClickHouse: streaming for real-time data, batch/pseudo-streaming for large files, and backfilling for historical data migration. Learn the challenges, tradeoffs, and solutions for each approach to ingest data into ClickHouse.

This guide covers everything you need to know about ClickHouse Materialized Views, from basic concepts to advanced optimization techniques. Learn how materialized views work as insert triggers, when to use them vs projections, and how to build high-performance analytics applications.

ClickHouse excels at going from zero to production but schema evolution becomes a complex infrastructure and data engineering problem that requires specialized teams. This video demonstrates how to transform this complex process into a simple, developer-friendly experience using Tinybird and AI assistance.