Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
- databricks
- streaming
- apache-spark
- spark
- event-hubs
- bigdata
- connector
- scala
- continuous
- ingestion
- real-time
- microsoft
- structured-streaming
- eventhubs
- kafka
- apache
- spark-streaming
- stream
- azure
Scala versions:
2.11
spark-streaming-eventhubs_examples 2.1.0
Group ID:
com.microsoft.azure
Artifact ID:
spark-streaming-eventhubs_examples_2.11
Version:
2.1.0
Release Date:
May 25, 2017
Licenses:
Files:
Developers:
libraryDependencies += "com.microsoft.azure" %% "spark-streaming-eventhubs_examples" % "2.1.0"
ivy"com.microsoft.azure::spark-streaming-eventhubs_examples:2.1.0"
//> using dep "com.microsoft.azure::spark-streaming-eventhubs_examples:2.1.0"
import $ivy.`com.microsoft.azure::spark-streaming-eventhubs_examples:2.1.0`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>spark-streaming-eventhubs_examples_2.11</artifactId> <version>2.1.0</version> </dependency>
compile group: 'com.microsoft.azure', name: 'spark-streaming-eventhubs_examples_2.11', version: '2.1.0'