Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
- databricks
- real-time
- scala
- microsoft
- bigdata
- apache-spark
- spark
- continuous
- streaming
- apache
- azure
- ingestion
- event-hubs
- stream
- eventhubs
- spark-streaming
- kafka
- connector
- structured-streaming
Scala versions:
2.11
spark-streaming-eventhubs_connector 1.6.3
Group ID:
com.microsoft.azure
Artifact ID:
spark-streaming-eventhubs_connector_2.11
Version:
1.6.3
Release Date:
Apr 11, 2017
Licenses:
Files:
Developers:
libraryDependencies += "com.microsoft.azure" %% "spark-streaming-eventhubs_connector" % "1.6.3"
ivy"com.microsoft.azure::spark-streaming-eventhubs_connector:1.6.3"
//> using dep "com.microsoft.azure::spark-streaming-eventhubs_connector:1.6.3"
import $ivy.`com.microsoft.azure::spark-streaming-eventhubs_connector:1.6.3`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>spark-streaming-eventhubs_connector_2.11</artifactId> <version>1.6.3</version> </dependency>
compile group: 'com.microsoft.azure', name: 'spark-streaming-eventhubs_connector_2.11', version: '1.6.3'
Documentation
13 Dependencies
-
2.3.0
-
org.apache.spark::spark-streaming provided1.6.3
-
0.10
-
junit:junit test4.12
-
1.6.3
-
1.6.3
-
1.10.8
-
1.6.1
-
1.6.1
-
1.11.3
-
2.2.4