Add Feast CLI / python SDK documentation

README.md

@@ -44,6 +44,12 @@ For Feast administrators:
 * [Installation quickstart](docs/install.md)
 * [Helm charts](charts/README.md) details
 
+For Feast end users:
+* [Creating features](docs/endusers.md)
+
+For Feast developers:
+* [Building the CLI](cli/README.md)
+
 ## Notice
 
 Feast is still under active development. Your feedback and contributions are important to us. Please check our [contributing guide](CONTRIBUTING.md) for details.

charts/README.md

@@ -58,7 +58,7 @@ kubectl delete persistentvolumeclaim feast-postgresql
 The following table lists the configurable parameters of the Feast chart and their default values.
 
 | var | desc | default | 
-| -- | -- | -- | 
+| --- | --- | --- | 
 | `core.image.registry` | core docker image registry | feast | 
 | `core.image.repository` | core docker image repository | feast-core | 
 | `core.image.tag` | core docker image version | 0.1.0 | 

docs/concepts.md

@@ -19,7 +19,6 @@ A Feature is an individual measurable property or characteristic of an Entity. I
 * Entity - It must be associated with a known Entity within Feast
 * ValueType - The feature type must be defined, e.g. String, Bytes, Int64, Int32, Float etc.
 * Requirements - Properties related to how a feature should be stored for serving and training
-* Granularity - Time series features require a defined granularity
 * StorageType - For both serving and training a storage type must be defined
 
 Feast needs to know these attributes in order to be able to ingest, store and serve a feature. A Feature is only a feature when Feast knows about it; This seems contrite, but it introduces a best practice whereby a feature only becomes available for ingestion, serving and training in production when Feast has added the feature to its catalog.

docs/endusers.md

@@ -0,0 +1,148 @@
+# Feast End Users Quickstart Guide
+
+## Pre-requisities
+
+* A working Feast Core: Consult your Feast admin or [install your own](install.md).
+* Feast CLI tools: Use [pre-built
+  binaries](https://github.com/gojek/feast/releases) or [compile your
+  own](../cli/README.md).
+
+Make sure your CLI is correctly configured for your Feast Core. If
+you're running a local Feast Core, it would be:
+```sh
+feast config set coreURI localhost
+```
+
+## Introduction
+
+There are several stages to using Feast:
+1. Register your feature
+2. Ingest data for your feature
+3. Query feature data for training your models
+4. Query feature data for serving your models
+
+## Registering your feature
+
+In order to register a feature, you will first need to register a:
+* Storage location (typically done by your Feast admin)
+* Entity
+
+All registrations are done using [specs](specs.md).
+
+### Registering an entity
+
+Then register an entity, which is for grouping features under a unique
+key or id. Typically these map to a domain object, e.g., a customer, a
+merchant, a sales region.
+
+[`wordEntity.yml`](../examples/wordEntity.yml)
+```
+name: word
+description: word found in shakespearean works
+```
+
+Register the entity spec:
+```sh
+feast apply entity wordEntity.yml
+```
+
+### Registering your feature
+
+Next, define your feature:
+
+[`wordCountFeature.yml`](../examples/wordCountFeature.yml)
+```
+id: word.count
+name: count
+entity: word
+owner: bob@feast.com
+description: number of times the word appears
+valueType:  INT64
+uri: https://github.com/bob/example
+dataStores:
+  serving:
+    id: REDIS1
+  warehouse:
+    id: BIGQUERY1
+```
+
+Register it:
+```sh
+feast apply feature wordCountFeature.yml
+```
+
+## Ingest data for your feature
+
+Feast supports ingesting feature from 4 type of sources:
+
+* File (either CSV or JSON)
+* Bigquery Table
+* Pubsub Topic
+* Pubsub Subscription
+
+Let's take a look on how to create an import job spec and ingest some data from a CSV file. 
+You may find more information on how to ingest data from different sources 
+here: [[Import Specs]](specs.md#import-spec)
+
+### Prepare your data
+`word_counts.csv`
+```csv
+count,word
+28944,the
+27317,and
+21120,i
+20136,to
+17181,of
+14945,a
+13989,you
+12949,my
+11513,in
+11488,that
+9545,is
+8855,not
+8293,with
+8043,me
+8003,it
+...
+```  
+
+And then upload it into your Google Storage bucket:
+
+```sh
+gsutil cp word_counts.csv gs://your-bucket
+```
+
+### Define the job import spec
+`shakespeareWordCountsImport.yml`
+```yaml
+type: file.csv
+sourceOptions:
+  path: gs://your-bucket/word_counts.csv
+entities:
+  - word
+schema:
+  entityIdColumn: word
+  timestampValue: 2019-01-01T00:00:00.000Z
+  fields:    
+    - name: count
+      featureId: word.count
+    - name: word  
+```
+
+### Start the ingestion job
+Next, use `feast` CLI to run your ingestion job, defined in 
+`shakespeareWordCountsImport.yml`:
+```sh
+feast jobs run shakespeareWordCountsImport.yml
+```
+
+You can also list recent ingestion jobs by running:
+```sh
+feast list jobs
+```
+
+Or get detailed information about the results of ingestion with:
+```sh
+feast get job <id>
+```
+