Data Warehouse V/S Data Lake

Data analytics has become a crucial tool for businesses to stay ahead of their competitors, and data plays a vital role in this. With the vast amount of data currently available, it’s important to effectively manage and store it in a safe and secure place. Data warehouses and Data lakes are a one-stop solution for storing enormous data in the easiest way possible. Both of them allow data storage without compromising on security but they have different functionality. Here are the main differences between these two:

  1. A data warehouse is a centralised system where data is extracted from various sources and stored for strategic use, whereas a data lake is a storage repository that is competent in storing a vast amount of data: be it structured, semi-structured, or unstructured.

  2. Data retention: In a data warehouse, a lot of time is consumed in making a structured data model and unwanted files are refused, whereas in a data lake, all the data can be kept regardless of its time of use.

  3. Users: Data warehouses are generally used by business analysts, whereas data lakes are mostly used by data scientists or data developers.

  4. Data capturing format: The data that sits in the data warehouse is highly structured and ready for analysis, whereas in a data lake data is stored in its native format without the need for defining schema.

  5. Data Governance: Data warehouse have more Data Governance rules to follow, Data lake has more flexible rules

  6. Cost: Data warehouse is more expensive than Data lake

  7. Data access: Data warehouse have more restricted access, Data lake have more open access to data for different teams.

To learn more about the difference between Data Warehouse and Data Lake, check out this article: Data Warehouse VS Data Lake

Which one do you think is the best solution if data storage, a Data Warehouse or a Data Lake? Share your thoughts and reasoning in the comments below!

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