when to used data lake vs data warehouse?

Deciding between a data lake and a data warehouse depends on the specific use case and the nature of the data involved.

Data Warehouse

• Ideal for structured data that requires complex queries and analytics operations 1.
• Suited for business intelligence and analytics tasks where data is already highly structured, such as financial transactions or CRM data 2.
• Limits: Requires data to be structured and transformed before loading, leading to potential stale data issues and higher costs for maintaining copies of data 3.
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    Data Lakes vs. Warehouses

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    2

    Evolution of Lakehouses

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    3

    Data Lakes vs. Warehouses

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    4

    Data Architecture Breakdown

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Data Lake

• Best for storing raw data, including structured, semi-structured, and unstructured data (e.g., sensor data, logs, text, and images) 1.
• Enables agile, real-time analytics and machine learning directly on raw data, which can be beneficial for modern AI applications 3.
• Eliminates the need for costly transformations prior to analysis, allowing for more flexible and broad data usage 4.

Data Lakehouse

• Combines the benefits of both, providing structure for analytics similar to a warehouse while retaining the flexibility of a lake 4.
• Supports various workloads, including real-time analytics, machine learning, and unstructured data, centralizing data processing on a single platform 2.

Choosing between these options typically involves considering the nature of your data and processing needs. A data warehouse is the best fit for structured data and traditional analytics, whereas a data lake is preferable for varied, real-time data needs. A data lakehouse offers a hybrid solution for diverse and dynamic workloads.