Data Lakes vs Data Warehouses


What is the ideal data lake design to replace a warehouse?

The point of this post is not to look at it in the general sense of the differences. There are many articles on the internet, and one can google for that. The point is to put it into the context of exploiting the massive difference, which enables it to replace a data warehouse. It will lay the foundation for an organization to adopt advanced technologies such as AI, Data Mining, ML, and eventually predictive analysis.


Key Differences

Criteria Lakes Warehouses Lake Advantage
Data Type
  • Structured
  • Semi-structured
  • Unstructured
Structured only Most corporate data is structured. So this point is not that significant but is still a difference
Transformation None to minimal (ideally none) Transformed Consume data in its raw form as soon as its ingested in the lake
Staging None Always Eliminate staging tables as there is no need for transformation
Latency Potentially zero. Data is immediately available for consumption Usually, one day or more as ETL and rebuilds are batch jobs The two above differences eliminate latency
Fact Tables Raw Built with Foreign-key references to Dimension level tables The FK’s are the same that exist in the source system as the fact tables
Business Views None. Left to data discovery and visualization layer Static, pre-defined There are no universe or business area definitions required before they are available for discovery and consumption
End-points for Discovery and Visualization Layer Many Usually one There are no constraints of either hardware or software to restrict the lakes to a single store, such as a back-end OLAP DB
Ongoing Changes Easy Complex & expensive There is no need to make changes to ETL, rebuild universes and business areas

The Massive Impact

As you can see, other than the first difference, all others enable a massive ability to provide significant benefits:

Benefits Why?
Real-Time Analytics Since there is no need to transform data, it is consumable immediately
Adaptability Multiple data formats for optimized query consumption (rows and columns)
Reduced costs for scalability, high availability, disaster recovery, and ongoing maintenance Unconstrained by expensive hardware and software licenses. Options are available for vendor-supported software, albeit much cheaper than proprietary software. Cheaper to make changes and enhancements
Performance Leverage high-performance modern SQL engine to execute queries. They scale linearly with the addition of computing power
Enhanced end-user experience Lightweight components leverage the heavy lifting done by a highly efficient distributed SQL Engine. There is no need to have intermediate UI caching or servers

Simplified Architecture

Data Lakes

Data Lakes

Data Lakes


Traditional Data Warehousing

Traditional data warehousing

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