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Remove hard coded resources in docs #696

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Sep 25, 2022
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Remove hard coded resources in docs #696

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c96fcb6
Remove hard coded resources in docs
Sep 22, 2022
3efe9e7
quick fix
Sep 23, 2022
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4 changes: 2 additions & 2 deletions docs/concepts/feathr-capabilities.md
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Expand Up @@ -143,8 +143,8 @@ schema = AvroJsonSchema(schemaStr="""
}
""")
stream_source = KafKaSource(name="kafkaStreamingSource",
kafkaConfig=KafkaConfig(brokers=["feathrazureci.servicebus.windows.net:9093"],
topics=["feathrcieventhub"],
kafkaConfig=KafkaConfig(brokers=["<replace_with_your_broker>.servicebus.windows.net:9093"],
topics=["<replace_with_your_eventhub>"],
schema=schema)
)

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2 changes: 1 addition & 1 deletion docs/concepts/feature-definition.md
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Expand Up @@ -23,7 +23,7 @@ See an examples below:

```python
batch_source = HdfsSource(name="nycTaxiBatchSource",
path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/green_tripdata_2020-04.csv",
path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/demo_data/green_tripdata_2020-04.csv",
event_timestamp_column="lpep_dropoff_datetime",
timestamp_format="yyyy-MM-dd HH:mm:ss")
```
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2 changes: 1 addition & 1 deletion docs/concepts/feature-registry.md
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Expand Up @@ -61,7 +61,7 @@ Alternatively, you can set the feature registry and the API endpoint in the conf
```yaml
feature_registry:
# The API endpoint of the registry service
api_endpoint: "https://feathr-sql-registry.azurewebsites.net/api/v1"
api_endpoint: "https://<replace_with_your_feathr_api_endpoint>.azurewebsites.net/api/v1"
```

### Register and List Features
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8 changes: 4 additions & 4 deletions docs/concepts/materializing-features.md
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Expand Up @@ -96,7 +96,7 @@ The API call is very similar to materializing features to online store, and here

```python
client = FeathrClient()
offlineSink = HdfsSink(output_path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/materialize_offline_test_data/")
offlineSink = HdfsSink(output_path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/materialize_offline_test_data/")
# Materialize two features into a Offline store.
settings = MaterializationSettings("nycTaxiMaterializationJob",
sinks=[offlineSink],
Expand All @@ -121,14 +121,14 @@ settings = MaterializationSettings("nycTaxiTable",
```

This will materialize features with cutoff time from `2020/05/10` to `2020/05/20` correspondingly, and the output will have 11 folders, from
`abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/materialize_offline_test_data/df0/daily/2020/05/10` to `abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/materialize_offline_test_data/df0/daily/2020/05/20`. Note that currently Feathr only supports materializing data in daily step (i.e. even if you specify an hourly step, the generated features in offline store will still be presented in a daily hierarchy). For more details on how `BackfillTime` works, refer to the [BackfillTime section](#feature-backfill) above.
`abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/materialize_offline_test_data/df0/daily/2020/05/10` to `abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/materialize_offline_test_data/df0/daily/2020/05/20`. Note that currently Feathr only supports materializing data in daily step (i.e. even if you specify an hourly step, the generated features in offline store will still be presented in a daily hierarchy). For more details on how `BackfillTime` works, refer to the [BackfillTime section](#feature-backfill) above.

You can also specify the format of the materialized features in the offline store by using `execution_configurations` like below. Please refer to the [documentation](../how-to-guides/feathr-job-configuration.md) here for those configuration details.

```python

from feathr import HdfsSink
offlineSink = HdfsSink(output_path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/materialize_offline_data/")
offlineSink = HdfsSink(output_path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/materialize_offline_data/")
# Materialize two features into a Offline store.
settings = MaterializationSettings("nycTaxiMaterializationJob",
sinks=[offlineSink],
Expand All @@ -141,7 +141,7 @@ For reading those materialized features, Feathr has a convenient helper function

```python
from feathr import get_result_df
path = "abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/materialize_offline_test_data/df0/daily/2020/05/20/"
path = "abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/materialize_offline_test_data/df0/daily/2020/05/20/"
res = get_result_df(client=client, format="parquet", res_url=path)
```

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4 changes: 2 additions & 2 deletions docs/concepts/point-in-time-join.md
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Expand Up @@ -105,12 +105,12 @@ And below shows the join definitions:
feature_query = FeatureQuery(
feature_list=["feature_X"], key=UserId)
settings = ObservationSettings(
observation_path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/green_tripdata_2020-04.csv",
observation_path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/demo_data/green_tripdata_2020-04.csv",
event_timestamp_column="Date",
timestamp_format="MM/DD")
client.get_offline_features(observation_settings=settings,
feature_query=feature_query,
output_path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/output.avro")
output_path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/demo_data/output.avro")
```

## Advanced Point-in-time Lookup
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4 changes: 2 additions & 2 deletions docs/quickstart_synapse.md
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Expand Up @@ -164,8 +164,8 @@ The following feature join config is used:
```python
feature_query = [FeatureQuery(feature_list=["f_location_avg_fare"], key=["DOLocationID"])]
settings = ObservationSettings(
observation_path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/green_tripdata_2020-04.csv",
output_path="abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/output.avro",
observation_path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/demo_data/green_tripdata_2020-04.csv",
output_path="abfss://{adls_fs_name}@{adls_account}.dfs.core.windows.net/demo_data/output.avro",
event_timestamp_column="lpep_dropoff_datetime", timestamp_format="yyyy-MM-dd HH:mm:ss")
client.get_offline_features(feature_query=feature_query, observation_settings=settings)
```
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