Data Lake vs Data Warehouse
Data Lake and Data Warehouse are both widely used for storing a huge volume of data, but they are not the same. A Data Lake is a wide pool of raw data which the main purpose is to store the data. Data Warehouse, on the other hand, is the repository of unstructured and it pre-processes the data for easy access to the user.
The main similarity between Data Lake and Data Warehouse is the high-level purpose of storing the data. Other than that, both are different in many ways.
| Data Lake | Data Warehouse |
|---|---|
| Data Lake stores huge volume of raw data | Though the input data might be raw, Data Warehouse processes the data and stores it |
| Data Lake stores data for future purposes | Data Warehouse serves as a repository of data which is currently in use |
| Data Lake is highly accessible and quick to update | Data Warehouse is more complicated and costly to make changes |
| Data in the Data Lake is accessed by Data Scientists to process the data | Data in the Data Warehouse is used by Business Professionals to find information |
| Data in Data Lake is mostly used for Predictive and Advanced analytics | Data in Data Warehouse in used for multi-purpose operations and performance analysis |
| Cost of implementation of Data Lake is high | Cost of implementation of Data Warehouse is lesser when compared to Data Lake |
| Data will take weeks or even months to reach the user | Data will take hours or in some cases days to reach the user |
| Data Lakes use ELT (Extract Load Transform) process | Data Warehouse use ETL (Extract Transform Load) process |
| Big data technology used in Data Lake is new | Data Warehouse concept, unlike Big Data, had been used for decades |
| Data is always kept in raw form. It will be transformed only when it is used | The biggest challenge in Data Warehouse in the inability to make changes in data |
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