Our Data Lakehouse is the high-performance engine that underpins our entire Data Platform. It combines the massive scale of a data lake with the reliability and performance of a data warehouse, creating a single, unified foundation for all your data. As a builder, you don't manage our Lakehouse directly; instead, you experience its benefits through the speed, reliability, and powerful features of Arkham's toolchain.
Our Lakehouse architecture is the "how" behind the seamless experience you have in our platform's UI tools. Each core technical feature of our Lakehouse directly enables a key part of your workflow.
This diagram reveals how our managed Lakehouse foundation powers Arkham's toolchain, translating core technical features like ACID transactions into the reliability and speed you experience in our platform.
To appreciate the benefits of Arkham's managed Lakehouse, it's helpful to compare it to other traditional architectures that data teams often have to build and maintain themselves. Arkham's platform is designed to give you the advantages of a Lakehouse without the setup and management overhead.
Feature
Data Lakes
Data Warehouses
Arkham Lakehouse (Best of Both)
Storage Cost
✅ Very low (S3)
❌ High (compute + storage)
✅ Very low (S3)
Data Formats
✅ Any format (JSON, CSV, Parquet)
❌ Structured only
✅ Any format + structure
Scalability
✅ Petabyte scale
❌ Limited by cost
✅ Petabyte scale
ACID Transactions
❌ No guarantees
✅ Full ACID support
✅ Full ACID support
Data Quality
❌ No enforcement
✅ Strong enforcement
✅ Strong enforcement
Schema Evolution
❌ Manual management
❌ Rigid structure
✅ Automatic evolution
Query Performance
❌ Slow, inconsistent
✅ Fast, optimized
✅ Fast, optimized
ML/AI Support
✅ Great for ML
❌ Poor ML support
✅ Great for ML
Real-time Analytics
❌ Batch processing
✅ Real-time queries
✅ Real-time queries
Time Travel
❌ Not available
❌ Limited versions
✅ Full version history
Setup Complexity
✅ Simple (but lacks features)
❌ Complex ETL
✅ Zero (Managed by Arkham)
For a deeper dive into the technical differences between these architectures, we recommend IBM's comprehensive article on Data Warehouses, Data Lakes and Lakehouses.