Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
- continuous
- structured-streaming
- event-hubs
- bigdata
- streaming
- eventhubs
- apache-spark
- kafka
- spark
- databricks
- ingestion
- microsoft
- azure
- stream
- spark-streaming
- connector
- apache
- real-time
- scala
Scala versions:
2.11
spark-streaming-eventhubs 2.1.4
Group ID:
com.microsoft.azure
Artifact ID:
spark-streaming-eventhubs_2.11
Version:
2.1.4
Release Date:
Sept 21, 2017
Licenses:
Files:
Developers:
libraryDependencies += "com.microsoft.azure" %% "spark-streaming-eventhubs" % "2.1.4"
ivy"com.microsoft.azure::spark-streaming-eventhubs:2.1.4"
//> using dep "com.microsoft.azure::spark-streaming-eventhubs:2.1.4"
import $ivy.`com.microsoft.azure::spark-streaming-eventhubs:2.1.4`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>spark-streaming-eventhubs_2.11</artifactId> <version>2.1.4</version> </dependency>
compile group: 'com.microsoft.azure', name: 'spark-streaming-eventhubs_2.11', version: '2.1.4'
Documentation
16 Dependencies
-
2.3.0
-
org.apache.spark::spark-streaming provided2.1.0
-
0.10
-
junit:junit test4.12
-
2.1.0
-
2.1.0
-
2.1.0
-
2.1.0
-
1.10.8
-
1.6.1
-
1.6.1
-
1.11.3
-
2.2.6