What is Caching?
Caching is an optimization technique used across nearly every layer of modern software and data engineering. It stores the result of an expensive operation (like a large SQL query or the rendering of a complex web page) in fast storage, typically memory, so the next request for the same result can be served without repeating the work.
The core idea of caching is simple: avoid doing the same expensive work twice.
If a CEO opens a Business Intelligence dashboard, the query engine (like Snowflake or Trino) might need to scan billions of rows of Apache Parquet data and run a distributed JOIN to calculate that Q3 revenue is $10,000,000. That query consumes CPU time and costs real cloud compute dollars.
If the CFO opens the same dashboard five seconds later, running the same scan again is wasteful. A cache avoids that repeated work.
The Architecture of the Cache Layer
A cache is often a lightweight in-memory data store (like Redis or Memcached) that sits in front of a slower primary database.
The Read-Through Mechanism
When the CFO requests the dashboard, the application follows a two-step path:
- The Cache Hit: The application checks Redis. If it finds the precomputed value
$10,000,000under the keyQ3_Revenue, it returns it in well under a millisecond. The primary database does no work for this request. - The Cache Miss: If the value is not in the cache (or has been evicted), the application queries the primary database, runs the calculation, returns the result, and writes a copy into the cache so the next request is fast.
Cache Invalidation: The Hard Part
A well-known saying, attributed to Phil Karlton, goes: “There are only two hard things in Computer Science: cache invalidation and naming things.”
The main danger of caching is stale data.
If the cache holds $10,000,000, but a large late-arriving sale is written to the primary database, the true figure is now $12,000,000.
If the system does not remove (invalidate) the old value from the cache, the CFO will see outdated numbers, which can lead to bad decisions.
Time-To-Live (TTL)
To limit staleness, engineers assign a Time-To-Live (TTL) to cached data. For example, they configure the cache to expire entries after 5 minutes. The dashboard stays fast, and cached data is never more than about 5 minutes behind the primary database.
Advanced Data Lakehouse Caching
Modern open Data Lakehouse architectures also cache inside the query engine. Dremio, for example, uses Reflections. Instead of caching a single dashboard number, Dremio stores optimized, sometimes pre-aggregated, columnar copies of the data. When an analyst writes a new ad-hoc SQL query, the query optimizer can recognize that a Reflection can answer it and read the Reflection instead of the raw data lake, speeding up ad-hoc exploration without application-level caching.
Summary of Technical Value
Caching is a core defense against unnecessary load. By serving repeated requests from fast memory instead of recomputing them, caching protects expensive backend databases from repetitive queries and keeps web applications and BI dashboards responsive under heavy traffic, as long as invalidation and expiry are handled carefully.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.