Data Lakehouse Patterns: Scaling Delta Lake with Apache Spark on Databricks

The Rise of the Lakehouse

For years, companies were forced to choose between the structure of a data warehouse and the scalability of a data lake. The Lakehouse architecture—pioneered by Delta Lake and Apache Spark—combines both, bringing ACID transactions and schema enforcement directly to cloud storage.

Dealing with the Small File Problem

High-throughput real-time streaming datasets create thousands of small files under 1MB. This destroys query performance due to metadata overhead. In Databricks, we address this with OPTIMIZE commands to trigger compaction:

OPTIMIZE delta_table
WHERE date = '2026-06-13'
ZORDER BY (userId)

This merges small files into larger ~1GB files and co-locates related data, boosting queries by 10x-100x.