This whitepaper is for historical reference only. Some content might be outdated and some links might not be available.

# Data lake design patterns and principles
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## Framework
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 Following is a high-level framework for building a data lake on AWS. 

### 10,000 foot view
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![This is a 10,000 foot (high level) view of how analytics systems work with source and destination systems.](https://docs.aws.amazon.com/whitepapers/latest/best-practices-building-data-lake-for-games/images/ten-thousand-foot.png)


### 5000 foot view
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![This is a 5,000 foot (mid-level) view of how analytics systems work with source and destination systems.](https://docs.aws.amazon.com/whitepapers/latest/best-practices-building-data-lake-for-games/images/five-thousand-foot.png)


 Diving deeper into the framework, there are data streamers, data collectors, data aggregators, and data transformers that collect the data from the data producers (sources). Depending on the use-case, data is then consumed for analysis or downstream consumers and cataloged into a data lake for governed access. 

### 1000 foot view
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![This is a 1,000 foot (detailed) view of how analytics systems work with source and destination systems.](https://docs.aws.amazon.com/whitepapers/latest/best-practices-building-data-lake-for-games/images/one-thousand-foot.png)


 Diving deeper in the framework, Some AWS services are added as an example to show data flow. This layout is a common pattern AWS observed with its customers.