Top differences between Data warehouse and Data Lake

Are you confused between a Data warehouse and a data lake? Both look and sound the same, right? Both store a large amount of data, then what is the difference between a data warehouse and a data lake? Well, this article will help you to clear the difference between these two.

Definition

As we know that data warehouse and data lake both store enormous amounts of data. But there is a major difference between them. A data lake stores a large amount of raw data but it is in the raw format. That means the data lake stores all types of data: structured, unstructured, or semi-structured. On the other hand, a data warehouse stores a large amount of only structured data.

Purpose

Data warehouse and Data Lake differ as they are invented for different purposes. Data warehouse data stores data which is used for different queries and analysis. Basically, the data warehouse transforms data into information.
Data Lake serves as a large container for data. It resembles the concept of a big lake where the lake has different sources. Same way, a data lake stores all types of data that may be generated through real-time logs, machine to machine data, streaming data.

Concept of storage

The data stored in the data warehouse and the data stored in the data lake differ in how they have stored the data in them. In a data warehouse, data is stored in the files and folders format so that data is stored in an organized manner. On the other hand, the data lake stores data in its raw state until the time it is needed to access.

Timeline for storage

The data warehouse and data lake differ in another important factor and that is storage timeline. Data in the data warehouse requires significant time for analysing data from different data sources whereas data in Data Lake can be retained all the time such that it can be used in the future also.

Costing

A data warehouse is a costlier affair than a data lake. Storing data in the data warehouse is expensive and time-consuming too.

Time to access data

Data warehouse grants slower access to its stored data. On contrary, Data Lake provides better agility, and flexibility so that users can access data lake data quickly. It provides self-service business intelligence and more opportunities for data exploration also.

Data Processing Method

There are key differences between data processing methods of data warehouse and Data Lake. Data warehouse uses traditional Extract Transform Load or ETL method to process the data. Data Lake uses Extract Load Transform or ELT method to process the raw data stored in it.

User

Even both the data warehouse and data lake store data the user who access the data are different from each other. Data stored in the data warehouse is structured so it is used by the operational users for creating reports or key performance metrics. Data in the data lake is of all types, so the users who access the data are mostly data scientists or data analysts.

Although data warehouse and Data Lake are used to store voluminous data, they cannot be interchanged. Hope the above contents have cleared the difference between these two world-changing technologies.