Data Lakehouse Architecture: Why It's Replacing Warehouses
A lakehouse unifies the flexibility of a data lake with the reliability of a warehouse. We compare Delta Lake, Iceberg, and Hudi.
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For years, organizations ran two separate systems: a data lake (cheap storage, flexible schemas, no ACID) and a data warehouse (expensive, structured, reliable). The lakehouse architecture eliminates this duality — bringing warehouse reliability to lake economics.
Why the Original Data Lake Failed
No ACID transactions. Two Spark jobs writing to the same partition simultaneously could corrupt data.
No schema evolution. Adding a column meant rewriting all existing data.
No data versioning. Once you overwrote a partition, the previous data was gone.
The lakehouse solves all three by adding a transaction log on top of object storage.
Delta Lake vs Iceberg vs Hudi
| Feature | Delta Lake | Apache Iceberg | Apache Hudi |
|---|---|---|---|
| ACID Transactions | ✓ | ✓ | ✓ |
| Time Travel | ✓ | ✓ | ✓ |
| Schema Evolution | ✓ | ✓ (best) | ✓ |
| Upsert Performance | Good | Good | Best |
| Vendor Neutral | Databricks-led | Apache | Apache |
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Delta Lake in Practice
Delta Lake has the best Spark integration:
from delta.tables import DeltaTable
delta_table = DeltaTable.forPath(spark, "s3://bucket/delta/events")
delta_table.alias("target").merge(
source=new_data.alias("source"),
condition="target.id = source.id"
).whenMatchedUpdateAll() \
.whenNotMatchedInsertAll() \
.execute()
# Time travel
df = spark.read.format("delta") \
.option("timestampAsOf", "2026-05-07") \
.load("s3://bucket/delta/events")When to Choose Which Format
Choose Delta Lake if: You're on Databricks or need the best upsert performance.
Choose Apache Iceberg if: You need engine flexibility (query with Trino, Flink, and Spark).
Choose Apache Hudi if: Your primary use case is CDC ingestion with high-frequency upserts.
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