Data warehouse and data lake both are the centralized storage on an enterprise. But the basic difference between data warehouse and data lake is that the data warehouse has the structured and pre-processed data contrary to this the data lake accommodates a heterogeneous data in its raw format.
Data in a data warehouse is analyzed for retrieving strategic information which helps in making decisions for the betterment of the enterprise, but the purpose of data inside data lake is not defined until it is acquired by any personnel in the enterprise for analysis.
In this section, we will try to identify every possible difference between a data warehouse and a data lake. We will also discuss both the terms separately in order to understand their behavioural aspect.
Content: Data Warehouse Vs Data Lake
Difference Chart
| Basis for Differentiation | Data Warehouse | Data Lake |
|---|---|---|
| Nature of Data | Data Warehouse has structured and pre-processed data. | Data lake has variant data in its raw format. |
| Users | Data analyst or business analyst mines the data warehouse to get the strategic data. | It supports all kind of users but data scientists like to analyze the data in data lake. |
| Cost | Data warehouse has high cost storage. | Data lake is developed for low cost storage and is scalable. |
| Use | The purpose of data in data warehouse is to provide strategic information. | The purpose of data in data lake is not well defined unless analyzed by someone. |
| Update | Addition and updation of data are complex. | Addition and updation of data are quick and easy. |
| History | Data ware is the conventional method of data storage. | Data lake is implemented using big data technology. |
What is a Data Warehouse?
A data warehouse can be defined as a centralized repository of an enterprise where the pre-processed data is stored. Data pre-processing incorporates data cleaning, data reduction and data transformation.
Data in a data warehouse come from disparate sources, but before it is stored in data warehouse it is processed and structured so that data analyst can extract strategic information from it easily. That’s why it is complex to store data in the data warehouse.
Data analyst or executives and managers are the target users of the data warehouse. They mine that data in the data warehouse to retrieve strategic information which helps them in taking decision for betterment of their enterprise.
What is Data Lake?
A data lake is a centralized storage of an enterprise where all the structured, as well as unstructured, data of the enterprise, is stored. Every kind of data is stored in the data lake in its raw format.
The data in the data lake is not processed before storing it into the data lake. That’s why the addition or updation of data tends to be easy in data lakes as compared to the data warehouse.
The data lake is accessible to all types of users but it most preferred by data scientist as they get the deep information here which is helpful to build an advanced model. The data in the data warehouse does not have any well-defined purpose unless it is acquired, processed and delivered by someone for a certain purpose.
Data lakes are scalable that means you can increase the size of storage by introducing new plugin storages and hence it is less expensive also. Therefore, it is easier to extend the storage of data lakes.
Data lakes can be accessed by any business user and they can easily acquire the data they require without the help of any IT personnel. Data lake almost complements most of the functionalities of data warehouse and that’s why it needs to be built alongside the data warehouse.
Key Takeaways
- The data warehouse has structure and pre-processed data as before storing any data to data warehouse it is cleaned, reduced and transformed. On the other side, the data is directly dumped to the data lake and it contains every type of data almost in its raw format.
- The data lake storage can be extended by introducing a new storage plugin and are less expensive when compared to the data warehouse.
- It is complex to add or update data in the data warehouse as it needs to be processed before it is stored to the data warehouse whereas it is easier to amend data from disparate sources in data lakes.
- Data in the data warehouse is analyzed by the data analyst or the managers and executives of the enterprise to retrieve strategic information using which they can make a decision for the betterment of the enterprise. On the other hand, data in the data lake is analyzed by the data scientist as he gets the data in the granular form which he can process to implements the advance model.
- The purpose of the data in the data warehouse is well defined as it is used to provide strategic information. On the other hand, the purpose of data in the data lake is not defined unless it is acquired, processed and analyzed by someone.
- A data warehouse is a conventional method to store data whereas the data lake is implemented using new technologies like big data.
Conclusion
We have observed the behaviour of both data warehouse and data lakes, both have complementary behaviour and that is why you cannot implement data lake alone it has to be built alongside a data warehouse.
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