Data Warehouse vs Data Lake: 2025 Decision Guide

The question everyone asks wrong

“Should we use a data warehouse or a data lake?” Wrong question. The right question is: what workloads do we have, what latency do we need, and how much are we willing to pay?

The answer is almost never “one or the other.” It is usually “both, with clear roles.” But to get there, you first need to understand what each thing is without the marketing layer every vendor applies.

Three architectures, no smoke

Data warehouse. A store for structured data, optimized for analytical queries. Data enters clean, transformed, and with a defined schema (schema-on-write). Query latency: sub-second to minutes. Examples: BigQuery, Snowflake, Redshift, Azure Synapse.

Good for: business dashboards, financial reporting, operational KPIs, any SQL query that needs fast answers over structured data.

Not good for: storing raw data, processing logs at scale, training machine learning models, storing images/video/audio.

Data lake. A store for data in any format (CSV, JSON, Parquet, images, logs), organized in a distributed file system (S3, GCS, ADLS). Data enters as-is, untransformed. Schema is applied at read time, not write time (schema-on-read). Query latency: seconds to minutes (with engines like Athena, Presto, Spark SQL).

Good for: massive low-cost storage, semi-structured data, data exploration, feature engineering for ML, historical archives.

Not good for: fast interactive queries, dashboards with 5-second refresh, business users who need immediate answers.

Data lakehouse. The convergence. A data lake with a management layer that provides warehouse-grade guarantees: ACID transactions, schema enforcement, time travel, indexes. Technologies like Apache Iceberg, Delta Lake, and Apache Hudi turn a data lake into something that behaves like a warehouse for analytical workloads, without losing the lake’s flexibility for ML workloads and unstructured data processing.

The lakehouse is the dominant trend in 2025, but it is not the answer to everything. For purely analytical workloads with business users, a dedicated warehouse still delivers better performance and better user experience.

Decision framework

Instead of choosing by instinct or by what your competitors use, we answer four questions.

Question 1: What kind of data do you have?

If 90% of your data is structured tables (transactions, inventory, customers, orders), a warehouse covers your primary need. The data lake would be complementary for logs, documents, and ML training data.

If you have a significant mix of structured and unstructured data (IoT, images, application logs, sensor data), the data lake is the foundation. You can put a warehouse on top for analytical queries, or use a lakehouse.

A pattern we have seen repeatedly: companies stuffing unstructured data into a warehouse because it is all they have. The result is a Snowflake bill of 8,000 euros/month when 60% of the data could sit in S3 for 200 euros/month. Expensive and unnecessary.

Question 2: Who will query the data?

Business users (directors, controllers, marketing) need standard SQL, low latency, and BI tools like Looker, Tableau, or Power BI. For them, a warehouse provides the best experience.

Data engineers and data scientists can work with Spark, Python, and query engines on the lake. They are technical users who do not need the warehouse abstraction.

If you have both groups (and nearly every mid-sized company does), the most efficient architecture is a lake as the foundation with a materialized warehouse layer for business queries.

Question 3: What latency do you need?

Operational dashboards with 5-10 second refresh: warehouse. No real alternative.

Daily or weekly reports: lake with batch processing. Cheaper, equally effective.

Ad-hoc exploratory queries: lake with a query engine (Athena, Trino). Latency of 5-30 seconds, sufficient for exploration.

ML models that need access to complete history: lake. The warehouse is not the place to serve training features to Spark.

Question 4: How much can you spend?

This is where the decision becomes concrete. Cost comparison for a typical mid-market data volume (5 TB of data, 100 queries/day):

Component	Warehouse (Snowflake)	Lake (S3 + Athena)	Lakehouse (S3 + Iceberg + Trino)
Storage	~200 EUR/month	~115 EUR/month	~115 EUR/month
Query compute	~800 EUR/month	~150 EUR/month	~300 EUR/month
Ingestion/ETL	~200 EUR/month	~100 EUR/month	~150 EUR/month
Estimated total	~1,200 EUR/month	~365 EUR/month	~565 EUR/month

The warehouse is 3x more expensive than the lake for the same data volume. But if your business users need interactive queries, the warehouse user experience justifies the premium. If your workloads are primarily batch and ML, the lake is significantly more economical.

The lakehouse occupies the middle ground: more expensive than a pure lake, but with the transactional guarantees and query experience that a raw lake does not offer.

Our recommendation by profile

Mid-sized company with classic analytics (reporting, dashboards, KPIs): Warehouse. BigQuery if you are already on GCP, Snowflake if you are multi-cloud, Redshift if you are on AWS and want to minimize cost. You do not need a lake unless you have ML workloads or significant unstructured data.

Company with mixed data and a strong technical team: Lakehouse. S3/GCS as the foundation, Iceberg or Delta Lake as the table format, Trino or Spark SQL for queries. Materialized views in the warehouse for business dashboards.

Data-intensive startup or company with ML in production: Lake as foundation, warehouse as serving layer. Most data lives in the lake (cheap, flexible). Data that needs to serve dashboards gets materialized into a warehouse or a fast serving layer.

Small company with less than 1 TB of data and no dedicated data team: You need neither a lake nor a lakehouse. A well-indexed PostgreSQL with Metabase on top is sufficient for basic analytics. Sometimes the best architecture is the simplest one.

The most common mistake

The mistake we see most frequently is not choosing the wrong technology. It is failing to define data governance before choosing the architecture. Without a data catalog, without quality policies, without clear ownership of each dataset, it does not matter whether you use Snowflake or a data lake: you will end up with a “data swamp,” a data lake where nobody knows what is in it, what is reliable, and what is stale.

Data architecture is not a technology problem. It is an organizational problem with a technical solution. And the right technical solution depends on your organization, not on what a vendor blog says.

To understand how data flows into your warehouse or lake, see our ETL vs ELT comparison. If you need help defining your data architecture, we start with diagnosis: what data you have, where it lives, who uses it, and what for. The technology comes after.