Data Warehouse vs Data Lake: Which Does Your Business Need?
"Should we build a data warehouse or a data lake?" is one of the most common questions we get from clients. The honest answer: it depends on your data maturity, team size, and what you're trying to achieve. This guide cuts through the jargon and helps you make the right call.
The Core Difference (Without the Jargon)
Data Warehouse
Structured, cleaned, ready to query
- • Schema-on-write (structure defined before storing)
- • Optimised for fast SQL queries
- • Best for: BI dashboards, regular reports
- • Tools: Snowflake, BigQuery, Redshift
Data Lake
Raw, flexible, store-everything approach
- • Schema-on-read (structure applied at query time)
- • Stores structured, semi-structured, unstructured data
- • Best for: ML training data, raw event storage
- • Tools: AWS S3 + Glue, Azure Data Lake, GCS
When to Choose a Data Warehouse
A data warehouse is the right choice when your primary need is answering known business questions reliably and quickly. If your leadership team asks "what were our sales by region last quarter?" every week, a data warehouse gives you that answer in seconds.
Choose a data warehouse if:
- You have structured data from defined sources (CRM, ERP, transactional databases)
- Your primary use case is business reporting and dashboards
- Your analysts know SQL and need fast, reliable query performance
- Data governance and compliance are important (GDPR, SOC 2)
- Your team is small and you want to get value quickly without deep data engineering
When to Choose a Data Lake
A data lake makes sense when you're dealing with high-volume, diverse data types and your use cases include machine learning, real-time streaming, or you genuinely don't know yet what questions you'll need to answer.
Choose a data lake if:
- You need to store raw clickstream data, IoT sensor data, or unstructured text/images
- Your team is building ML models that need large training datasets
- You want to preserve raw data for future reprocessing as requirements evolve
- You're dealing with data volumes in the terabytes+ range
- You have data engineers comfortable with Spark, Python, and cloud-native tooling
The Modern Answer: The Data Lakehouse
Here's the thing — most modern businesses end up needing both. The data lakehouse architecture (popularised by Databricks and now supported by Snowflake, BigQuery, and others) gives you the best of both worlds: the flexibility of a data lake with the performance and governance of a data warehouse.
The Lakehouse Approach
Store raw data in cheap cloud storage (S3/GCS/ADLS), apply transformations with tools like dbt, and serve structured data to BI tools via your warehouse layer. This pattern is now the industry standard for data-mature organisations.
Our Recommendation by Business Stage
Startup / Early Stage (0–500K rows/day)
Start with BigQuery or Snowflake. Cheap, fast to set up, serverless. Don't over-engineer.
Growth Stage (500K–50M rows/day)
Add a raw landing zone in S3/GCS. Use dbt for transformations. Consider Databricks if ML is on the roadmap.
Scale Stage (50M+ rows/day)
Full lakehouse architecture. Dedicated data engineering team. Real-time streaming with Kafka or Kinesis.
The Bottom Line
If you're early-stage and just need reliable reporting, start with a cloud data warehouse. You can always add a data lake layer later. The biggest mistake we see is companies jumping straight to complex lakehouse architectures before they have the data volume or team to justify it. Get value fast, then scale the architecture.
Not sure which architecture is right for you?
Let's assess your current data setup
In a 30-minute call, we'll look at your current setup and tell you exactly what you need — and what you don't.