## SageMaker Feature Store Champions Workshop - Module 2 ### Working with Offline store * Look at data in S3 console * Athena query for dataset extraction (via Athena console) * Athena query for dataset extraction (programmatically in SageMaker notebooks) * Extract a training dataset and storing in S3 * Use row-level time travel utility to easily extract point-in-time correct data given a dataframe of events #### Notebook: * m2_nb1_feature_store_dataset_extraction.ipynb ### Search and Discovery using Feature-Level Metadata * Retrieve feature group * Update feature's metadata (description and parameters) * Search for feature using its custom metadata (using Boto3 API and Amazon SageMaker Studio) #### Notebook: * m2_nb2_feature_metadata_search_discovery.ipynb ### Apache Iceberg offline store compaction * Offline store compaction using Amazon Athena * Offline store compaction using Spark * Scheduled compaction #### Notebook: * m2_nb3_offline_iceberg_compaction.ipynb