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Difference between Data lake and Data warehouse
Data Lake and Data Warehouse both are used for storing big data. A Data Lake is a very big storage repository which is used to store raw unstructured data, machine to machine, logs flowing through in real-time. The purpose of the stored data is not defined in a data lake. They are stored for future analysis of the data.
A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. A Data warehouse collects the data from multiple sources and transforms the data using ETL process, then loads it to the Data Warehouse for business purpose.
Read this tutorial to learn more about Data Lake and Data Warehouse and how they are different from each other.
What is a Data Lake?
A Data lake is a very large storage repository in which all sorts of data are stored at a low cost. Data lake is basically used to store raw and unstructured data. Therefore, the data stored in a data lake is independent of the source of information. They can be transformed into any form at any time whenever required. Data in a data lake is not in the normalized form.
Data lakes are mainly used to store extremely large volumes of structured and unstructured data such as call logs, ERP transactions, etc. The major advantage of using data lakes is that they store data in raw form, hence this data can be analyzed in new ways to obtain unexpected insights.
What is a Data Warehouse?
A Data Warehouse is a large storage repository of data that is collected from different organizations within a corporation. It represents a time variant, non-volatile and integrated set of data which assists the management in the decision making process. A data warehouse stores structured and filtered data. It uses a centralized system for data storage.
Data warehouses use slightly denormalized data and follow top-down data model. The important properties of a data warehouse include flexibility, longer life, data orientation, etc. But it is a difficult task to design a data warehouse, as they have a continuously evolving structure.
Difference between Data Lake and Data Warehouse
The following table highlights all the key differences between data lake and data warehouse −
Key |
Data Lake |
Data Warehouse |
|---|---|---|
Basic |
A data lake is a very big storage repository which is used to store raw unstructured data machine to machine, logs flowing through in real-time. |
A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose |
Normalized |
Data is not in normalized form |
Data warehouse has denormalized schema |
Schema Creation |
Schema is created after data is loaded |
Schema is created before the data is loaded |
ELT/ETL |
It used ELT process |
It used ETL process |
Uses |
It is ideal for those who want in-depth analysis |
It is good for operational users |
Conclusion
The most significant difference is that a data lake is a very large storage repository which is used to store raw unstructured data, while a data warehouse is a repository for structured data.
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