diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
index 64714f0b799fc5347b9d7bdaf30d9b3e0a55b895..d6d5605948a5a7e4ee9dd15530f9298904f2a794 100644
--- a/docs/streaming-kafka-integration.md
+++ b/docs/streaming-kafka-integration.md
@@ -29,7 +29,7 @@ Next, we discuss how to use this approach in your streaming application.
             [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume])
 
     You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$)
-	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
+	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
 	</div>
 	<div data-lang="java" markdown="1">
 		import org.apache.spark.streaming.kafka.*;
@@ -39,7 +39,7 @@ Next, we discuss how to use this approach in your streaming application.
             [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]);
 
     You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html)
-	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
+	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
 
 	</div>
 	<div data-lang="python" markdown="1">
@@ -105,7 +105,7 @@ Next, we discuss how to use this approach in your streaming application.
 			streamingContext, [map of Kafka parameters], [set of topics to consume])
 
 	See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$)
-	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala).
+	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala).
 	</div>
 	<div data-lang="java" markdown="1">
 		import org.apache.spark.streaming.kafka.*;
@@ -116,7 +116,7 @@ Next, we discuss how to use this approach in your streaming application.
 				[map of Kafka parameters], [set of topics to consume]);
 
 	See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html)
-	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java).
+	and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java).
 
 	</div>
 	</div>
@@ -153,4 +153,4 @@ Next, we discuss how to use this approach in your streaming application.
 
 	Another thing to note is that since this approach does not use Receivers, the standard receiver-related (that is, [configurations](configuration.html) of the form `spark.streaming.receiver.*` ) will not apply to the input DStreams created by this approach (will apply to other input DStreams though). Instead, use the [configurations](configuration.html) `spark.streaming.kafka.*`. An important one is `spark.streaming.kafka.maxRatePerPartition` which is the maximum rate at which each Kafka partition will be read by this direct API. 
 
-3. **Deploying:** Similar to the first approach, you can package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR and the launch the application using `spark-submit`. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation.
\ No newline at end of file
+3. **Deploying:** Similar to the first approach, you can package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR and the launch the application using `spark-submit`. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation.