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Data Warehouse vs. Data Lake vs. Lakehouse: bit.ly/3QQkb3L
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In our latest KZread video, we break down these essential components of modern data architecture, offering insights into their features, use cases, and future trends.
We'll be talking about:
1. Data Warehouse: is a centralized repository where data from various sources is consolidated, transformed, and stored for query and analysis purposes.
2. Data Lake: is a storage repository that contains a large volume of raw data in its native format.
3. Lakehouse: is a hybrid model that combines the best features of both Data Lakes and Data Warehouses.
You can't miss this if you are planning to work or already working as a Data Engineer/Analytic/Manager/ or ELT Developer. You can often find these skills in most job roles and in Interview Questions.
Here are the top three most commonly asked hands-on interview questions that you can expect related to Data Lake, Data Warehouse, Lakehouse:
1. What are the key factors to consider when designing a dimensional model for a healthcare data warehouse? How would you ensure data quality and consistency?
2. A retail company wants to build a data lake to store various types of data including customer transactions, clickstream data, and inventory data. How would you design the architecture of this data lake? Can you discuss the different storage options available for a data lake and explain which one(s) would be suitable for this retail company's requirements?
3. Could you explain how the lakehouse architecture addresses the limitations of traditional data warehouses and data lakes? Provide examples of how this architecture benefits analytics and data governance in the financial sector.
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DataWarehouse
DataLake
LakeHouse
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MachineLearning
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See you in the next video!