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‎docs/.gitbook/assets/feast-on-aws-3- (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (5).png‎

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‎docs/.gitbook/assets/image (4) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (3) (3) (3) (3) (5).png‎

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‎docs/.gitbook/assets/image (6) (3) (3) (3) (3) (3) (3) (3) (3) (3) (2) (1) (7).png‎

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‎docs/.gitbook/assets/point_in_time_join (1) (2) (2) (3) (3) (3) (3) (6).png‎

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‎docs/.gitbook/assets/untitled-25-1- (2) (2) (2) (3) (3) (3) (3) (3) (3) (1) (6).jpg‎

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‎docs/community.md‎

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# Community
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{% hint style="success" %}
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**Office Hours:** Have a question, feature request, idea, or just looking to speak to a real person? Come and join the [Feast Office Hours](https://calendly.com/d/gc29-y88c/feast-chat-w-willem-and-jay) on Friday and chat with a Feast contributor!
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**Office Hours:** Have a question, feature request, idea, or just looking to speak to a real person? Come and join the [Feast Office Hours](https://calendly.com/d/gc29-y88c/chat-with-feast-maintainers) on Friday and chat with a Feast contributor!
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{% endhint %}
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## Links & Resources

‎docs/concepts/data-model-and-concepts.md‎

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# Data model
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### Dataset
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## Dataset
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A dataset is a collection of rows that is produced by a historical retrieval from Feast in order to train a model. A dataset is produced by a join from one or more feature views onto an entity dataframe. Therefore, a dataset may consist of features from multiple feature views.
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**Dataset vs Feature View:** Feature views contain the schema of data and a reference to where data can be found \(through its data source\). Datasets are the actual data manifestation of querying those data sources.
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**Dataset vs Data Source:** Datasets are the output of historical retrieval, whereas data sources are the inputs. One or more data sources can be used in the creation of a dataset.
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### Feature References
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## Feature References
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Feature references uniquely identify feature values in Feast. The structure of a feature reference in string form is as follows: `<feature_table>:<feature>`
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Feature references uniquely identify feature values in Feast. The structure of a feature reference in string form is as follows: `<feature_table>:<feature>`
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Feature references are used for the retrieval of features from Feast:
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It is possible to retrieve features from multiple feature views with a single request, and Feast is able to join features from multiple tables in order to build a training dataset. However, It is not possible to reference \(or retrieve\) features from multiple projects at the same time.
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### **Entity key**
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## **Entity key**
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Entity keys are one or more entity values that uniquely describe an entity. In the case of an entity \(like a `driver`\) that only has a single entity field, the entity _is_ an entity key. However, it is also possible for an entity key to consist of multiple entity values. For example, a feature view with the composite entity of \(customer, country\) might have an entity key of \(1001, 5\).
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Entity keys are one or more entity values that uniquely describe an entity. In the case of an entity \(like a `driver`\) that only has a single entity field, the entity _is_ an entity key. However, it is also possible for an entity key to consist of multiple entity values. For example, a feature view with the composite entity of \(customer, country\) might have an entity key of \(1001, 5\).
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![](../.gitbook/assets/image%20%2815%29.png)
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Entity keys act as primary keys. They are used during the lookup of features from the online store, and they are also used to match feature rows across feature views during point-in-time joins.
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### Event timestamp
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## Event timestamp
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The timestamp on which an event occurred, as found in a feature view's data source. The entity timestamp describes the event time at which a feature was observed or generated.
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The timestamp on which an event occurred, as found in a feature view's data source. The entity timestamp describes the event time at which a feature was observed or generated.
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Event timestamps are used during point-in-time joins to ensure that the latest feature values are joined from feature views onto entity rows. Event timestamps are also used to ensure that old feature values aren't served to models during online serving.
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### Entity row
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## Entity row
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An entity key at a specific point in time.
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![](../.gitbook/assets/image%20%2811%29.png)
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### Entity dataframe
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## Entity dataframe
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A collection of entity rows. Entity dataframes are the "left table" that is enriched with feature values when building training datasets. The entity dataframe is provided to Feast by users during historical retrieval:
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‎docs/feast-on-kubernetes/advanced-1/security.md‎

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### Overview
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![Overview of Feast&apos;s Security Methods.](../../.gitbook/assets/untitled-25-1-%20%282%29%20%282%29%20%282%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%281%29%20%283%29.jpg)
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![Overview of Feast&apos;s Security Methods.](../../.gitbook/assets/untitled-25-1-%20%282%29%20%282%29%20%282%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%283%29%20%281%29%20%284%29.jpg)
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Feast supports the following security methods:
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‎docs/feast-on-kubernetes/getting-started/install-feast/kubernetes-amazon-eks-with-terraform.md‎

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* Amazon EMR cluster to run Spark \(3x spot m4.xlarge\)
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* Staging S3 bucket to store temporary data
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![](../../../.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%282%29.png)
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![](../../../.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%283%29.png)
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### 1. Requirements
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‎docs/quickstart.md‎

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3. Materialize feature values from the offline store into the online store.
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4. Read the latest features from the online store for inference.
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### Install Feast
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## Install Feast
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Install the Feast SDK and CLI using pip:
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```bash
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pip install feast
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```
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### Create a feature repository
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## Create a feature repository
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Bootstrap a new feature repository using `feast init` from the command line:
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Bootstrap a new feature repository using `feast init` from the command line:
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```text
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feast init feature_repo
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Creating a new Feast repository in /home/Jovyan/feature_repo.
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### Register feature definitions and deploy your feature store
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## Register feature definitions and deploy your feature store
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The `apply` command registers all the objects in your feature repository and deploys a feature store:
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### Generating training data
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## Generating training data
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The `apply` command builds a training dataset based on the time-series features defined in the feature repository:
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### Load features into your online store
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## Load features into your online store
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The `materialize` command loads the latest feature values from your feature views into your online store:
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### Fetching feature vectors for inference
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## Fetching feature vectors for inference
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### Next steps
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## Next steps
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* Follow our [Getting Started](getting-started/) guide for a hands tutorial in using Feast
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* Join other Feast users and contributors in [Slack](https://slack.feast.dev/) and become part of the community!

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