When to Choose Data Lake vs Data Warehouse vs Lakehouse

The Real Question: The decision is not just technical, it's about your workload mix, team maturity, and cost tolerance. Each pattern optimizes for different constraints. Choose Data Lake When: Your primary challenge is volume and variety. You're ingesting hundreds of TB per day from dozens of heterogeneous sources: application logs, clickstreams, Internet of Things (IoT) sensors, third party feeds. Upfront schema modeling would be a blocker because source formats change frequently or are semi structured. Query patterns are exploratory or batch oriented. Data scientists running ad hoc analyses on years of historical data, overnight ETL jobs aggregating metrics, or ML training jobs that need access to raw feature data. You can tolerate query latencies in the tens of seconds to minutes range. Your team has strong data engineering skills. Managing file formats, partition strategies, compaction jobs, and schema evolution requires expertise. But the payoff is massive cost savings: storing 5 PB in a lake costs roughly 10,000 to 15,000 dollars per month versus 100,000 to 125,000 dollars in a traditional warehouse. Choose Data Warehouse When: Your primary need is interactive query performance and concurrent user support. Business analysts running dashboards, executives needing sub second response times for key metrics, applications querying aggregates in real time. Data volume is moderate (under 100 TB) or you can curate a subset of hot data. Warehouses excel at structured, frequently accessed data with predictable query patterns. You value simplicity: load data, define schemas, write Structured Query Language (SQL), get results. No Spark clusters or partition tuning. Your team is analytics focused, not infrastructure focused. Managed warehouses like BigQuery, Snowflake, or Redshift handle optimization, scaling, and maintenance. You pay more per TB but save engineering time. Choose Lakehouse When: You want both flexibility and performance. Lakehouse architectures (using table formats like Delta Lake, Iceberg, or Hudi) add database features on top of lake storage: ACID transactions, schema enforcement, time travel, and efficient updates and deletes. This matters when you need to update dimension tables frequently (like customer profiles changing daily) or handle Change Data Capture (CDC) streams that require upserts. Traditional lakes force you to rewrite entire partitions for small updates. Lakehouses handle this efficiently. The trade off is complexity and metadata overhead. Lakehouses require managing transaction logs, compaction jobs to merge small files, and vacuum operations to clean up old versions. They also need newer compute engines that understand the table format. Decision Framework with Numbers: If over 80 percent of your queries are interactive (under 10 seconds target latency) and data volume is under 50 TB, warehouse wins on simplicity. If you are storing over 500 TB with mostly batch workloads (minutes to hours latency tolerance), data lake saves 5x to 10x on storage and compute costs. If you need frequent updates (more than 1000 updates per second on fact tables) plus analytical queries, lakehouse patterns become necessary. Pure lakes struggle with small file churn, pure warehouses get expensive at high update volume. In practice, many companies run both: warehouse for curated, high performance analytics (maybe 20 TB of hot data), lake for long term storage and exploratory work (maybe 2 PB of historical data). The lakehouse pattern aims to unify these, but adds operational complexity that only pays off at scale.