You will hear a lot about data warehouse and data lake when you work on Big Data. Both are widely used for storing Big Data but, they are not interchangeable. In this article, we will check data warehouse and data lake, its definition and differences.
Data Warehouse and Data Lake
As mentioned earlier, both are used for storing big data. But, they server different purpose when it comes to data usage.
Data Warehouse
A Data warehouse is an electronic storage of business data for analysis. It is a technique for collecting and managing data from various heterogeneous sources to provide meaningful business insights. Data warehouse development follows a certain life cycle similar to other projects such as front-end applications. Data warehouse is usually used to store the structured and processed data as per the requirements.
You can use different data warehouse design approaches to design data warehouse and various extraction methods are used in the data warehouse.
Data Lake
A data lake is a vast pool of raw, granular data, the purpose for which is not yet defined. Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data.
Data lakes are usually configured on a cluster of inexpensive and scalable commodity hardware. This allows data to be dumped in the lake in case there is a need for it later without having to worry about storage capacity. The clusters could either exist on-premises or in the cloud.
The data lake benefits more for the one who perform deep analysis on raw data. A data architect or scientists use data lake to make business critical decisions using raw data.
A data lake works on the schema-on read principle. Many organizations use Hadoop and Hive to create Data lake. Relational databases such as Snowflake allows you to create data lake as it has a special data type to handle semi-structured data.
Difference between Data Warehouse and Data Lake
There are several differences between data warehouse and data lake. Following are the key differences.
Data warehouse vs Data Lake
| Parameter | Data Lake | Data Warehouse |
| Data Structure | Stores structured, semi-structured and unstructured data. | Stores structured data. |
| Purpose of Data | Purpose is not yet defined | Data is currently in use |
| Data Users | Usually, data architects and scientists | Business Professionals and decision makers |
| Data Timeline | Contains all data | Usually, contains only relevant data. |
| Data processing | Data Lakes use of the ELT (Extract Load Transform) process. | Data Warehouse use both ETL and ELT (Extract Load Transform) process. |
Storage and Data Structure
Data lake stores raw data which is not processed and its purpose is not yet defined. Whereas Data warehouse stores processed data and data has a purpose.
A data lake store unstructured, semi-structured and structured data. Whereas data warehouse mostly store structured data.
Purpose of Data
Data in a data lake has no fixed purpose. It will store the data from multiple sources. Whereas data warehouse store processed data and it is in use.
Data Users
Data scientists use the data stored lake for deep analysis. Whereas the data warehouse is ideal for operational users because the data is well structured, easy to use and understand.
Data Retention
Data lake contains all the data. Whereas the data warehouse store only relevant and processed data.
Hope this helps 🙂