Analytics Infrastructure Guide
Build a ClickHouse analytics stack that scales to billions of events
An engineering guide to building a production analytics infrastructure using ClickHouse as the core OLAP engine. Covers event ingestion design, ClickHouse table engines and sharding, dbt transformation pipelines, Metabase/Grafana integration, query optimization, and cost management at scale.
Inside the guide
What You'll Learn
ClickHouse Table Design
MergeTree vs ReplacingMergeTree vs AggregatingMergeTree — when to use each, with schema examples and benchmarks.
Ingestion Pipeline
Kafka → ClickHouse ingestion with deduplication guarantees, backfill strategy, and schema evolution handling.
dbt Transformation Layer
dbt-clickhouse setup, incremental model patterns, and materialized view strategy for pre-aggregation.
Query Optimization
EXPLAIN analysis, projection indexes, skipping indexes, and partition pruning with real query examples before/after.
Table of Contents
Who This Is For
Written by engineers, for engineers
Senior Engineer
Building production systems and tired of re-inventing the wheel on every project.
Software Architect
Needs battle-tested patterns to back architectural decisions with evidence.
Startup CTO
Must ship fast without accumulating technical debt that kills you later.
The Problem
PostgreSQL breaks under analytical query loads at 100M+ rows — the migration to ClickHouse is non-trivial and poorly documented
ClickHouse's MergeTree family is powerful but choosing the wrong table engine causes performance regressions that are hard to reverse
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Complete Guide
- Lifetime PDF access
- 500+ page guide
- dbt project + ClickHouse schemas
- Video walkthroughs (10h)
- Free updates for 2 years
Frequently Asked Questions
Is this guide applicable to self-hosted ClickHouse or only ClickHouse Cloud?
Both. Chapters 3-6 cover self-hosted deployment (Docker, Kubernetes), and Chapter 7 covers ClickHouse Cloud with cost optimization specifics.
Does the guide cover real-time vs batch analytics trade-offs?
Yes. Chapter 2 is entirely dedicated to the lambda vs kappa architecture decision for analytics workloads, with ClickHouse positioned in each scenario.