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GitBook: [v0.11-branch] 79 pages and 7 assets modified
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woop authored and gitbook-bot committed Aug 9, 2021
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2 changes: 1 addition & 1 deletion docs/feast-on-kubernetes/concepts/architecture.md
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# Architecture

![](../../.gitbook/assets/image%20%286%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%282%29%20%281%29%20%283%29.png)
![](../../.gitbook/assets/image%20%286%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%282%29%20%281%29%20%284%29.png)

## Sequence description

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2 changes: 1 addition & 1 deletion docs/feast-on-kubernetes/concepts/overview.md
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## Concept Hierarchy

![](../../.gitbook/assets/image%20%284%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%283%29%20%283%29%20%283%29%20%283%29%20%285%29.png)
![](../../.gitbook/assets/image%20%284%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%283%29%20%283%29%20%283%29%20%283%29%20%286%29.png)

Feast contains the following core concepts:

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* Amazon EMR cluster to run Spark \(3x spot m4.xlarge\)
* Staging S3 bucket to store temporary data

![](../../../.gitbook/assets/feast-on-aws-3-%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29.png)
![](../../../.gitbook/assets/feast-on-aws-3-%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%282%29%20%281%29.png)

## 1. Requirements

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Expand Up @@ -56,7 +56,7 @@ In the example below there are two tables \(or dataframes\):

The user would like to have the driver features joined onto the entity dataframe to produce a training dataset that contains both the target \(trip\_completed\) and features \(average\_daily\_rides, maximum\_daily\_rides, rating\). This dataset will then be used to train their model.

![](../../.gitbook/assets/point_in_time_join%20%281%29%20%282%29%20%282%29%20%283%29%20%283%29%20%283%29%20%283%29%20%284%29.png)
![](../../.gitbook/assets/point_in_time_join%20%281%29%20%282%29%20%282%29%20%283%29%20%283%29%20%283%29%20%283%29%20%285%29.png)

Feast is able to intelligently join feature data with different timestamps to a single entity dataframe. It does this through a point-in-time join as follows:

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