Data lakes and data warehouses are both big data storage systems used by organizations to manage their petabytes of data. However, they differ in terms of their purpose, structure, and usage.
What are Data warehouses? (Ex: Amazon Redshift, Snowflake ) - Storage optimized for analysis: It is a centralized repository of structured data that has been optimized for reporting and analysis. - Predefined relational database schema - Facilitate complex querying and analysis.
Use Case: - When want to store structured data that has been processed, cleaned, and transformed for analysis and reporting - Want to support business intelligence, reporting, and data analysis.
What are Data lakes? (AWS S3, Azure Data, Apache Hadoop/HDFS) - Store raw data: It is a large, centralized repository of unstructured and structured data - Allows to store data in its native format without prior transformation. - Store a wide variety of data: Store large volumes of data from a variety of sources, including structured, semi-structured, and unstructured data.
Use Case : - You want to support multiple use cases across the organization, such as analytics, data science, and ad hoc analysis . - Want to store large amounts of data in their raw and unprocessed format to enable future analysis.
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1minuteconcepts
Data Warehouse vs Data Lake? [Big Data Series]
Data lakes and data warehouses are both big data storage systems used by organizations to manage their petabytes of data. However, they differ in terms of their purpose, structure, and usage.
What are Data warehouses? (Ex: Amazon Redshift, Snowflake )
- Storage optimized for analysis: It is a centralized repository of structured data that has been optimized for reporting and analysis.
- Predefined relational database schema
- Facilitate complex querying and analysis.
Use Case:
- When want to store structured data that has been processed, cleaned, and transformed for analysis and reporting
- Want to support business intelligence, reporting, and data analysis.
What are Data lakes? (AWS S3, Azure Data, Apache Hadoop/HDFS)
- Store raw data: It is a large, centralized repository of unstructured and structured data
- Allows to store data in its native format without prior transformation.
- Store a wide variety of data: Store large volumes of data from a variety of sources, including structured, semi-structured, and unstructured data.
Use Case :
- You want to support multiple use cases across the organization, such as analytics, data science, and ad hoc analysis .
- Want to store large amounts of data in their raw and unprocessed format to enable future analysis.
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Detailed article : lnkd.in/dwW56pvE
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Questions : Is there any other key difference worth highlighting ?
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1 year ago | [YT] | 2
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