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
azure-eventhubs-spark 2.1.6
Group ID:
com.microsoft.azure
Artifact ID:
azure-eventhubs-spark_2.11
Version:
2.1.6
Release Date:
Nov 7, 2017
Licenses:
Files:
Developers:
libraryDependencies += "com.microsoft.azure" %% "azure-eventhubs-spark" % "2.1.6"
ivy"com.microsoft.azure::azure-eventhubs-spark:2.1.6"
//> using dep "com.microsoft.azure::azure-eventhubs-spark:2.1.6"
import $ivy.`com.microsoft.azure::azure-eventhubs-spark:2.1.6`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>azure-eventhubs-spark_2.11</artifactId> <version>2.1.6</version> </dependency>
compile group: 'com.microsoft.azure', name: 'azure-eventhubs-spark_2.11', version: '2.1.6'
