diff --git a/kafka-streams-samples/kafka-streams-word-count/README.adoc b/kafka-streams-samples/kafka-streams-word-count/README.adoc
index 82cbc94..e6e1e4e 100644
--- a/kafka-streams-samples/kafka-streams-word-count/README.adoc
+++ b/kafka-streams-samples/kafka-streams-word-count/README.adoc
@@ -6,6 +6,7 @@ The example is based on the word count application from the https://github.com/c
 It uses a single input and a single output.
 In essence, the application receives text messages from an input topic and computes word occurrence counts in a configurable time window and report that in an output topic.
 This sample uses lambda expressions and thus requires Java 8+.
+The sample uses a default timewindow of 30 seconds.
 
 === Running the app:
 
@@ -15,7 +16,7 @@ Go to the root of the repository.
 
 `./mvnw clean package`
 
-`java -jar target/kafka-streams-word-count-0.0.1-SNAPSHOT.jar --spring.cloud.stream.kafka.streams.timeWindow.length=60000`
+`java -jar target/kafka-streams-word-count-0.0.1-SNAPSHOT.jar`
 
 Assuming you are running the dockerized Kafka cluster as above.
 
@@ -29,13 +30,4 @@ On another terminal:
 
 Enter some text in the console producer and watch the output in the console consumer.
 
-Time window can be changed using the following property.
-
-`spring.cloud.stream.kafka.streams.timeWindow.length` (value is expressed in milliseconds)
-
-In order to switch to a hopping window, you can use the `spring.cloud.stream.kstream.timeWindow.advnceBy` (value in milliseconds).
-This will create an overlapped hopping windows depending on the value you provide.
-
-Here is an example with 2 overlapping windows (window length of 10 seconds and a hop (advance) by 5 seconds:
-
-`java -jar target/kafka-streams-word-count-0.0.1-SNAPSHOT.jar --spring.cloud.stream.kafka.streams.timeWindow.length=10000 --spring.cloud.stream.kafka.streams.timeWindow.advnceBy=5000`
\ No newline at end of file
+Once you are done, stop the Kafka cluster: `docker-compose down`
\ No newline at end of file
diff --git a/kafka-streams-samples/kafka-streams-word-count/src/main/java/kafka/streams/word/count/KafkaStreamsWordCountApplication.java b/kafka-streams-samples/kafka-streams-word-count/src/main/java/kafka/streams/word/count/KafkaStreamsWordCountApplication.java
index b18f201..7e6fcf9 100644
--- a/kafka-streams-samples/kafka-streams-word-count/src/main/java/kafka/streams/word/count/KafkaStreamsWordCountApplication.java
+++ b/kafka-streams-samples/kafka-streams-word-count/src/main/java/kafka/streams/word/count/KafkaStreamsWordCountApplication.java
@@ -22,7 +22,6 @@ import org.apache.kafka.streams.kstream.KStream;
 import org.apache.kafka.streams.kstream.Materialized;
 import org.apache.kafka.streams.kstream.Serialized;
 import org.apache.kafka.streams.kstream.TimeWindows;
-import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.boot.SpringApplication;
 import org.springframework.boot.autoconfigure.SpringBootApplication;
 import org.springframework.cloud.stream.annotation.EnableBinding;
@@ -43,9 +42,6 @@ public class KafkaStreamsWordCountApplication {
 	@EnableBinding(KafkaStreamsProcessor.class)
 	public static class WordCountProcessorApplication {
 
-		@Autowired
-		private TimeWindows timeWindows;
-
 		@StreamListener("input")
 		@SendTo("output")
 		public KStream<?, WordCount> process(KStream<Object, String> input) {
@@ -54,7 +50,7 @@ public class KafkaStreamsWordCountApplication {
 					.flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
 					.map((key, value) -> new KeyValue<>(value, value))
 					.groupByKey(Serialized.with(Serdes.String(), Serdes.String()))
-					.windowedBy(timeWindows)
+					.windowedBy(TimeWindows.of(30000))
 					.count(Materialized.as("WordCounts-1"))
 					.toStream()
 					.map((key, value) -> new KeyValue<>(null, new WordCount(key.key(), value, new Date(key.window().start()), new Date(key.window().end()))));