Choosing Between Data Warehouse, Data Lake, and Lakehouse

The choice between warehouse, lake, and lakehouse depends on your latency requirements, data variety, governance needs, and cost constraints. Each architecture makes different tradeoffs that fit specific workload profiles.

Data warehouses deliver the strongest SQL performance with subsecond to low single digit second query latencies, tightly governed schemas, and optimized indexes for point lookups and highly selective queries. Business Intelligence (BI) dashboards that need interactive responsiveness depend on this performance. The tradeoff is expensive storage and compute (often 10x to 20x the cost of object storage), limited support for unstructured data like images or logs, and rigid schemas that resist rapid evolution. Use warehouses when BI dashboards need subsecond response, queries are highly selective on well understood schemas, and you can afford the premium.

Data lakes offer the cheapest storage at $20 to $30 per TB per month, flexible schema on read for diverse formats (JSON, Parquet, images, video), and massive scale supporting ML training and exploratory analytics. Freshness typically ranges from minutes to hours, acceptable for offline batch processing. The downside is weaker governance without additional tooling, slower SQL performance for interactive queries, and risk of data swamps. Choose lakes when workloads are ML heavy or exploratory, data types are diverse and evolving, and you can tolerate slower query times.

Lakehouses unify the flexibility of lakes with warehouse like reliability. They provide ACID transactions, schema enforcement and evolution, upserts and deletes for CDC, and time travel over open storage formats. This enables one pipeline for batch and streaming with minute level freshness (often 5 to 15 minutes as seen in Uber's Hudi deployment) and reduces data duplication between lake and warehouse. The tradeoff is operational complexity: managing transaction logs, compaction schedules, and snapshot retention. BI performance approaches warehouses for large scans but may lag for small point lookups. Use lakehouses when you need transactions and upserts over lake data, concurrent multi engine access, governance without duplicating into a warehouse, and can accept minute level rather than subsecond freshness.

💡 Key Takeaways

•Warehouse: subsecond to 2 second queries, 10x to 20x storage cost vs lakes, best for interactive BI with highly selective queries on curated schemas

•Lake: $20 to $30 per TB per month, minutes to hours freshness, supports diverse formats (JSON, Parquet, images), ideal for ML and exploratory analytics

•Lakehouse: ACID transactions, upserts and deletes, minute level freshness (5 to 15 minutes typical), reduces data duplication, unified batch and streaming

•Cost vs performance: object storage plus open compute minimizes vendor lock in and storage cost, but performance tuning (compaction, clustering, caching) consumes engineering time

•Freshness vs efficiency: frequent small commits improve freshness but create small files problem, batching and compaction improve scan efficiency but add write latency

📌 Examples

Uber lakehouse with Apache Hudi: 5 to 15 minute end to end freshness, petabyte scale, hundreds of TB per day, upserts for compliance deletes without full rewrites

Typical warehouse: Snowflake or BigQuery serving BI dashboards with subsecond queries, cost $500 to $2000 per TB per month including compute, tightly governed schemas

Typical lake: S3 based storage at $23 per TB per month, Spark jobs running hourly batch processing, ML feature stores with minute to hour freshness

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