A recent ITBusinessEdge article examines the key differences between data lakes and data warehouses.

To summarize, the data warehouses are designed to store structured data that has been processed, cleansed, integrated, and transformed into a format for reporting and data analysis. Data warehouses store both current and historical data in a single place, and the data is typically accessed and used more frequently. Data lakes store structured, semi-structured, or unstructured data – usually in its native format, rather than a format for reporting and analysis – until it is needed. Because it uses a flat architecture to store data, querying is generally simple and fast, even with massive datasets. 

So, which one is right for your business? Because the tools are designed for different tasks, their functions are not entirely the same. Read on to determine what best suits your needs. 

Data Type: Unstructured vs. Processed Data 

As mentioned above, the data in a data lake is not processed before being stored. This makes a data lake ideal for big data analytics and machine learning, while a data warehouse can only be used for more limited analysis and reporting. 

Purpose: Undetermined vs. Determined

The purpose is another differentiator. Data lakes are flexible, and companies can use the data for any purpose, while data stored in a data warehouse is already determined and used. In addition, a data warehouse might contain structured data from an existing ERP or other application.

Database Schema: Schema-on-Read vs. Schema-on-Write 

A data warehouse follows a schema-on-write model, in which tables are created in advance to store data. If the table organization needs to be changed or updated down the road, it poses difficulties as all of the queries using that table will also require updating. With a data lake, which utilizes the schema-on-read model, the database can store any information in any column it wants. New data types can be added as new columns, and existing columns can change without affecting the system. 

Users: Data Scientist vs. Business or Data Analysts 

A data warehouse is designed for business users or analysts to explore the data to gain insights and answer specific business questions. There are no restrictions on how information can be used in a data lake because the data is not intended for any single use case. This means that users must assume responsibility for curating the data themselves before analysis and ensure its quality before storing it in this format.

Head to ITBusinessEdge to determine whether a data lake or a data warehouse is suitable for you.