Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
- microsoft
- databricks
- stream
- structured-streaming
- eventhubs
- bigdata
- apache
- streaming
- kafka
- apache-spark
- ingestion
- azure
- connector
- continuous
- real-time
- scala
- spark
- event-hubs
- spark-streaming
Scala versions:
2.11
azure-eventhubs-databricks 3.4.0
Group ID:
com.microsoft.azure
Artifact ID:
azure-eventhubs-databricks_2.11
Version:
3.4.0
Release Date:
Nov 28, 2017
Licenses:
Files:
libraryDependencies += "com.microsoft.azure" %% "azure-eventhubs-databricks" % "3.4.0"
ivy"com.microsoft.azure::azure-eventhubs-databricks:3.4.0"
//> using dep "com.microsoft.azure::azure-eventhubs-databricks:3.4.0"
import $ivy.`com.microsoft.azure::azure-eventhubs-databricks:3.4.0`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>azure-eventhubs-databricks_2.11</artifactId> <version>3.4.0</version> </dependency>
compile group: 'com.microsoft.azure', name: 'azure-eventhubs-databricks_2.11', version: '3.4.0'
