Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
- ingestion
- event-hubs
- microsoft
- streaming
- azure
- structured-streaming
- spark
- eventhubs
- stream
- spark-streaming
- scala
- real-time
- bigdata
- connector
- apache-spark
- databricks
- continuous
- kafka
- apache
Scala versions:
2.11
spark-streaming-eventhubs 1.6.0
Group ID:
com.microsoft.azure
Artifact ID:
spark-streaming-eventhubs_2.10
Version:
1.6.0
Release Date:
Sep 14, 2016
Licenses:
Files:
libraryDependencies += "com.microsoft.azure" %% "spark-streaming-eventhubs" % "1.6.0"
ivy"com.microsoft.azure::spark-streaming-eventhubs:1.6.0"
//> using dep "com.microsoft.azure::spark-streaming-eventhubs:1.6.0"
import $ivy.`com.microsoft.azure::spark-streaming-eventhubs:1.6.0`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>spark-streaming-eventhubs_2.10</artifactId> <version>1.6.0</version> </dependency>
compile group: 'com.microsoft.azure', name: 'spark-streaming-eventhubs_2.10', version: '1.6.0'
Documentation
10 Dependencies
-
org.apache.spark::spark-streaming provided1.6.2
-
0.10
-
junit:junit test4.12
-
1.6.2
-
1.6.2
-
1.10.8
-
1.6.1
-
1.6.1
-
1.11.3
-
2.2.4