Question 1

What's the difference between ClickHouse OLAP and ClickHouse OLAP Analytics?

Accepted Answer

OLAP = basics (write SQL, run queries). Analytics = production architecture (streaming pipelines, real-time aggregations, scale to 100B+ rows). Equivalent: learning React vs architecting Netflix. One is fundamentals, other is expertise.

Question 2

How do I build a Kafka → ClickHouse pipeline?

Accepted Answer

Setup: (1) Kafka cluster producing events. (2) ClickHouse with Kafka table engine (reads from Kafka). (3) Materialized View (transforms/aggregates as data streams in). (4) Storage table (stores aggregated data). Result: data lands in ClickHouse, pre-aggregated, queryable in <1s. Latency: event → dashboard = 5-10 seconds.

Question 3

What are Materialized Views and when are they critical?

Accepted Answer

Materialized View = continuously-updated aggregation table. Example: count clicks per page per minute (updated live). Without: raw events (1B rows), query takes 10s. With: pre-aggregated table (1M rows), query takes 1ms. For real-time dashboards: materialized views are essential.

Question 4

How do I optimize a slow ClickHouse query on 100B rows?

Accepted Answer

Steps: (1) EXPLAIN query (see query plan), (2) check partition pruning (is it scanning all data?), (3) add indexes (primary key optimization), (4) use appropriate sampling (if accuracy is negotiable), (5) parallelize (distributed query). Common: 100B row query goes 30s → 100ms via optimization.

Question 5

What's sharding and why does ClickHouse need it?

Accepted Answer

Sharding = horizontal partitioning (spread data across servers). Without: single server bottleneck (query speed saturates at ~500M rows/query). With: distribute across 10 servers (10x speedup). Complexity: clients must route to correct shard, re-balance data if servers added. Worth it for: 100B+ datasets.

Question 6

Can ClickHouse do JOINS on large tables?

Accepted Answer

Technically yes. Practically: slow (JOINS don't parallelize well). Better: denormalize at write-time (store all data needed in one table) or use small lookup tables for JOINS. Anti-pattern: JOINING two 10B-row tables (don't do this).

Question 7

What salary for ClickHouse Analytics mastery?

Accepted Answer

Data architect ($150-200k) + ClickHouse = $220-300k. Tech lead at scale-up ($180-240k) owning analytics = $250-350k. Scarcest skill globally: <200 specialists with 3+ years ClickHouse production experience. If you have this, you're top 0.1% of data engineers. Compensation reflects scarcity.

지역	주니어	미들	시니어
USA	$120k	$210k	$320k
UK	£95k	£170k	£270k
EU	€102k	€185k	€290k
CANADA	C$145k	C$250k	C$385k

지역	주니어	미들	시니어
USA	$120k	$210k	$320k
UK	£95k	£170k	£270k
EU	€102k	€185k	€290k
CANADA	C$145k	C$250k	C$385k

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