Data warehouses versus lakes for analytical workloads

A warehouse commonly provides managed tables, SQL performance and governance. A data lake commonly stores files in object storage with flexible formats and engines. Modern platforms blur the labels, so choose from access patterns, schema control, concurrency, governance and cost.

Reconcile two local representations

The data-engineering lab stores 3,000 invented events in SQLite and local Parquet. It computes daily region events, accounts and amount from both.

from data_engineering_cases import warehouse_lake_case

result = warehouse_lake_case()
assert result["aggregates_reconcile"] is True
assert result["warehouse_sql_rows"] == result["lake_parquet_rows"] == 270
print(result["parquet_bytes"])

Both produce 270 aggregate rows. The Parquet file is 96,676 bytes in the recorded run. SQLite and a temporary file only represent patterns; this is not a cloud warehouse or object-storage benchmark.

Match workload and control

Warehouses suit governed SQL access, concurrent reporting and curated tables. Lakes support inexpensive durable raw and semi-structured storage and multiple compute engines, but file layout, small files, schema evolution and catalog governance become critical.

Model development often needs both raw history and stable curated features. Define ownership and access at each layer. Store open formats where portability matters and test engine compatibility.

Avoid architecture by label

Measure query patterns, data volume, freshness, update behavior, concurrency, team skills, security and total cost. A “lakehouse” name does not remove the need for contracts, transactions and quality gates.

The Data Science course uses reconciliation to keep architecture discussions connected to correct model inputs.

Exercise

List five real analytical queries and their freshness/concurrency requirements. Design warehouse, lake and hybrid options, then compare governance and operational work.

Continue learning

This article is part of the Data engineering for data science sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Batch versus streaming data for an actual prediction need.
• Continue with Partition a dataset around common access patterns.

Reference: Apache Parquet overview.