Data Lake and Data Warehouse: Are They the Same Thing?

 

No, data lake and data warehouse are not synonymous terms, nor is data lake simply an updated version of data warehouse. The only thing they have in common is that they are both data repositories.

 

Let's look at what distinguishes the two.

 

Structure of Data

A warehouse's data is structured and processed. A lake's data can be structured, semi-structured, unstructured, or raw. The latter simply stores data in any form, whereas the former has a set of rules for storing data.

 

Storage Costs

Storage in Hadoop, which is used by data lakes, is less expensive than storage in data warehouses. Because Hadoop is open source, licencing and community support are free. Furthermore, Hadoop is designed to run on low-cost commodity hardware. Although the cost of warehouse storage has decreased dramatically over time, the labour required for data structuring remains prohibitively expensive.

 

Approach to Processing

To enter data into a warehouse, you must use a schema-on-write approach, which means the data must be modelled and shaped before it is entered. However, for a lake, you don't need to think twice before dumping data into it - simply load it in whatever shape you want. When you want to retrieve and use it, structure or model it; this is known as the schema-on-read approach.

 

Flexibility

Because there are no set rules for data lakes, any query, model, or app can be easily modified. On the contrary, changing the structure of something in a data warehouse will take time and effort because it is linked to other business processes.

 

Safety

Because data warehouses are the traditional data repositories and have been around for a long time, they are safe. When it comes to data security, data lakes are still in their infancy compared to warehouses.

 

Users

Given the stage of maturity that data lakes are currently at, it is primarily data scientists who are flocking to use them. Due to the costs involved, data warehouses are not accessible to everyone.

 

The objectives of the two data repositories are not the same. As a result, choose with the end goal in mind.

 

Have thoughts that differ from those expressed here? We'd love to hear your thoughts. Please leave them in the comments section.

 

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