Dernière mise à jour: 20 mai 2023
According to Google, interest in “Big Data» has been on the rise for several years. But it has really gained momentum this past year.
The aim of this article is to highlight the differences between Data Lakes and Data Warehouses.
Data Lakes and Data Warehouse are all designed to store Big Data. However, these two types of data storage are very different.
In fact, the only real similarity between them is their ability to store data.
What is Data Warehouse?
The term Data Warehouse translates into French as “Data warehouse”. Like a real warehouse, the Data Warehouse allows to collect, order, and store information from operational databases.
This allows businesses to improve decision making by making queries to examine trends in their customers.
What is Data Lake?
The term Date Lake translates into French as “data lake.” It is a data storage method also used by big data. Unlike a Data Warhouse, the data is kept in its original formats or is very little transformed. Data lake allows storage of raw data from various sources.
3 differences between a data lake and a data warehouse
Next, let's highlight three key differences between Data Warehouse and Data Lake. There are a number of key differentiators between a data lake and a data warehouse, here are three of those factors:
Data Lake stores raw data, Date Warehouse stores transformed data
Raw data is data that has not yet been analyzed and used for a specific purpose.
Perhaps the biggest difference between data lakes and data warehouses is the difference in structure between raw data and transformed data : Data lakes typically store unprocessed raw data, while data warehouses store transformed and cleaned data.
Like Data Lakes, Data Warehouses can store a large amount of data. However, their storage requires a minimum structure of them, that is to say that it is a question of reworking them in order to go from “raw data” to “net data”.
Data Lakes keeps all data
During the development of a data warehouse, considerable time is spent on analyzing and understanding data.
Generally, if the data is not used to answer specific questions or in a defined report, it can be excluded from the Wahrehouse data.
This is usually done to simplify the data model and also to save cost.storage space servers.
However, the Date Lake keeps ALL data.
Not just data that is used today, but data that can be used and even data that can never be used simply because it MIGHT be used one day.
This approach becomes possible because the hardware of a Data Lake generally differs a lot from that used for a Data Warehouse.
Data Lake easily adapts to changes
One of the main cons about the Data Wharehouse is the time taken to modify them.
Considerable time is spent up front during development to get the warehouse structure right.
Good warehouse design can accommodate change, but due to the complexity of the data loading process and the work done to facilitate analysis and reporting, these changes necessarily consume some developer resources and take time.
Many business questions can't wait that the Data Wharehouse team adapts its system to respond.
The data lakes have no structure and are therefore easy to consult and modify ; data changes can be made very quickly, as data lakes have very few restrictions.
So users can explore the data in new ways and answer their questions very quickly.