What is Lakehouse Architecture?

The Core Problem: Companies like Netflix and Uber were maintaining two separate systems. Data lakes (on Amazon S3 or similar) stored petabytes of raw data cheaply but offered no guarantees: no transactions, inconsistent schemas, and slow queries because engines had to scan millions of files. Data warehouses provided fast analytics and strong consistency but cost 10x to 100x more per terabyte and struggled with unstructured data. This forced a painful workflow: raw data lands in the lake, then teams sync terabytes to warehouses nightly for analytics, then sync results back. This doubled storage costs, created consistency nightmares (which version is correct?), and added 6 to 24 hour delays. The Solution: Lakehouse adds a metadata layer directly on top of lake storage. Instead of treating S3 as a dump of files, you treat it as managed tables with schemas, transactions, and snapshots. Three open formats emerged: Delta Lake, Apache Iceberg, and Apache Hudi. All three let you run SQL queries and get warehouse performance while keeping data in cheap object storage. How It Works: You still store data as columnar files like Parquet on S3. But now a table format manages metadata that describes which files belong to each table, what the schema is, and what changed in each transaction. Query engines (Spark, Trino, Flink) read this metadata first to understand table structure, then scan only relevant files. You get ACID guarantees, time travel to previous versions, and sub second queries, all without moving data to an expensive warehouse.