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-databricks 3.3.0
Group ID:
com.microsoft.azure
Artifact ID:
azure-eventhubs-databricks_2.11
Version:
3.3.0
Release Date:
Nov 7, 2017
Licenses:
Files:
Developers:
libraryDependencies += "com.microsoft.azure" %% "azure-eventhubs-databricks" % "3.3.0"
ivy"com.microsoft.azure::azure-eventhubs-databricks:3.3.0"
//> using dep "com.microsoft.azure::azure-eventhubs-databricks:3.3.0"
import $ivy.`com.microsoft.azure::azure-eventhubs-databricks:3.3.0`
<dependency> <groupId>com.microsoft.azure</groupId> <artifactId>azure-eventhubs-databricks_2.11</artifactId> <version>3.3.0</version> </dependency>
compile group: 'com.microsoft.azure', name: 'azure-eventhubs-databricks_2.11', version: '3.3.0'
