06-reference

dedp cache pattern

Fri Apr 03 2026 20:00:00 GMT-0400 (Eastern Daylight Time) ·book-chapter ·source: https://www.dedp.online/part-2/5-dep/cache-pattern.html ·by DEDP / Simon Späti

DEDP 5.2 — Cache Pattern

The foundational Data Engineering Pattern. Caching is so pervasive in data engineering that you are almost certainly implementing it without naming it. Materialized views, OBTs, OLAP cubes, dbt tables, semantic layers, data warehouses themselves — all are caching strategies. This chapter makes that explicit.

Definition

“A process that stores multiple copies of data or files in a temporary storage location — or cache — so they can be accessed faster.”

The cache pattern addresses the von Neumann bottleneck: the gap between compute speed and memory access speed. Every data engineering optimization that pre-computes or pre-stores results is a cache.

Where You Already Use It

ImplementationCaching Strategy
Materialized ViewsPre-computed query results stored on disk
One Big Table (OBT)Denormalized wide table eliminating joins
Traditional OLAP CubesPre-aggregated dimensional data
Modern OLAP (ClickHouse, Druid)Columnar storage optimized for analytical queries
dbt tablesMaterialized transformation outputs
Operational Data Store (ODS)Near-real-time cache of operational data
Semantic LayersLogical cache with optional materialization
Data WarehousesThe entire DWH is a cache of operational systems

This table is the chapter’s most valuable insight — it reframes technologies you already use as instances of a single pattern. See 06-reference/2026-04-04-dedp-mv-obt-dbt-olap-dwa for the convergent evolution of these implementations.

Implementation Options

Redis: In-memory key-value store. Instant read/write, multiple data structures, persistence and replication. Dominates the caching landscape.

Memcached: Lightweight distributed memory caching for database/API results. Simpler than Redis, good for reducing web application load.

Cube Store: Semantic layer cache built on Apache Parquet (storage) + Apache Arrow (in-memory) + DataFusion (query execution). Replaced Redis after hitting scalability bottlenecks — uses atomic domain-specific instructions instead of long command batches, distributed LRU caching, columnar compression.

The Cube Store evolution is telling — even purpose-built caching tools eventually need to be re-architected when the underlying access patterns change.

Advantages

Challenges

Query specificity: Only identical queries benefit. Different filters need separate cache entries. This is the same granularity problem OLAP cubes face — and why the 06-reference/2026-04-04-dedp-dynamic-queries pattern exists.

Data freshness: The critical tradeoff. As data changes, cached content risks becoming stale. Every cache implicitly answers: “How old is acceptable?” This is a business decision, not a technical one.

Update complexity: Idempotent cache updates require sophisticated partitioning. Aggregated data needs algorithms ensuring consistent reconciliation across fact and dimension tables.

Expertise requirements: Building and maintaining cache layers demands cross-disciplinary knowledge: storage, networking, data engineering, software engineering.

Alternatives (That Are Also Caches)

Modern query engines (DuckDB, WebAssembly): Provide near-real-time access through optimized columnar/vector execution without explicit cache maintenance. But the columnar format itself is a caching strategy.

Streaming architectures (Kafka, Flink): Continuous processing with live updates. But Kafka’s log is a cache. Flink’s state stores are caches. The pattern is inescapable.

Martin Kleppmann’s Designing Data-Intensive Applications treats caching synonymously with “denormalized, derived data” and categorizes OLAP cubes as specialized materialized views within the caching umbrella.

The Takeaway

The cache pattern is not optional — it is inherent to data engineering. The question is never “should we cache?” but “where in the stack do we cache, how fresh does it need to be, and who manages invalidation?”

This pattern is the foundation for the 06-reference/2026-04-04-dedp-dynamic-queries design pattern, which shows how caching integrates with semantic layers and unified APIs to solve the flexibility-vs-performance tension.

Connections