feast-dev
diff --git a/‎module_3/README.md‎
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diff --git a/‎module_3/docker-compose.yml‎
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diff --git a/‎module_3/feature_repo/data/driver_stats.parquet‎
-57.4 KB b/‎module_3/feature_repo/data/driver_stats.parquet‎
-57.4 KB
diff --git a/‎module_3/kafka_demo/Dockerfile‎
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diff --git a/‎module_3/kafka_demo/driver_stats.parquet‎
-57.4 KB b/‎module_3/kafka_demo/driver_stats.parquet‎
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diff --git a/‎module_3/kafka_demo/kafka_demo.py‎
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@@ -82,15 +82,11 @@ Start up the Docker daemon and then use Docker Compose to spin up the services a
 ```console
 $ docker-compose up
 
-Creating network "module_1_default" with the default driver
-Creating zookeeper ... done
+Creating network "module_3_default" with the default driver
 Creating redis     ... done
-Creating broker               ... done
 Creating feast_feature_server ... done
-Creating feast_push_server    ... done
-Creating kafka_events         ... done
 Creating registry             ... done
-Attaching to zookeeper, redis, broker, feast_push_server, feast_feature_server, kafka_events, registry
+Attaching to redis, feast_feature_server, registry
 ...
 ```
 
@@ -203,7 +199,7 @@ There are two broad approaches with streaming
    - This means that Feast only needs to know about a "batch feature" because the assumption is those batch features are sufficiently fresh.
    - **BUT** there are limits to how fresh your features are. You won't be able to get to minute level freshness.
 2. **[Complex, very fresh features]** Build separate streaming pipelines for very fresh features
-   - It is on you to build out a separate streaming pipeline (e.g. using Spark Structured Streaming or Flink), ensuring the transformation logic is consistent with batch transformations, and calling the push API as per module 1. 
+   - It is on you to build out a separate streaming pipeline (e.g. using Spark Structured Streaming or Flink), ensuring the transformation logic is consistent with batch transformations, and calling the push API as per [module 1](../module_1/README.md). 
 
 Feast will help enforce a consistent schema across batch + streaming features as they land in the online store. 
 
 
@@ -1,59 +1,12 @@
 ---
 version: '3'
 services:
-  zookeeper:
-    image: confluentinc/cp-zookeeper:7.0.1
-    container_name: zookeeper
-    environment:
-      ZOOKEEPER_CLIENT_PORT: 2181
-      ZOOKEEPER_TICK_TIME: 2000
-
-  broker:
-    image: confluentinc/cp-kafka:7.0.1
-    container_name: broker
-    ports:
-      - "9092:9092"
-      - "29092:29092"
-    depends_on:
-      - zookeeper
-    environment:
-      KAFKA_BROKER_ID: 1
-      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
-      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_INTERNAL:PLAINTEXT
-      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092,PLAINTEXT_INTERNAL://broker:29092
-      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
-      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
-      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
-
   redis:
     image: redis
     container_name: redis
     ports:
       - "6379:6379"
 
-  kafka_events:
-    build:
-      context: .
-      dockerfile: kafka_demo/Dockerfile
-    depends_on:
-      - broker
-    container_name: kafka_events
-
-  push_server:
-    container_name: feast_push_server
-    build:
-      context: .
-      dockerfile: feature_repo/Dockerfile
-    depends_on:
-      feature_server:
-        condition: service_healthy
-      registry:
-        condition: service_healthy
-    ports:
-      - "6567:6566"
-    links:
-      - redis
-
   feature_server:
     container_name: feast_feature_server
     build: