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 2.3.14-beta-6
Group ID:
com.microsoft.azure
Artifact ID:
azure-eventhubs-spark_2.11
Version:
2.3.14-beta-6
Release Date:
Aug 29, 2019
Licenses:
Files:
Developers:
libraryDependencies += "com.microsoft.azure" %% "azure-eventhubs-spark" % "2.3.14-beta-6"
ivy"com.microsoft.azure::azure-eventhubs-spark:2.3.14-beta-6"
//> using dep "com.microsoft.azure::azure-eventhubs-spark:2.3.14-beta-6"
import $ivy.`com.microsoft.azure::azure-eventhubs-spark:2.3.14-beta-6`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>azure-eventhubs-spark_2.11</artifactId> <version>2.3.14-beta-6</version> </dependency>
compile group: 'com.microsoft.azure', name: 'azure-eventhubs-spark_2.11', version: '2.3.14-beta-6'
