Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs
- apache-spark
- microsoft
- streaming
- structured-streaming
- bigdata
- stream
- event-hubs
- scala
- azure
- databricks
- connector
- spark
- continuous
- ingestion
- kafka
- real-time
- eventhubs
- apache
- spark-streaming
Scala versions:
2.11
azure-eventhubs-spark v2.3.hack-for-spark-aggregator.1
Group ID:
io.github.tilumi
Artifact ID:
azure-eventhubs-spark_2.11
Version:
v2.3.hack-for-spark-aggregator.1
Release Date:
Apr 18, 2019
Licenses:
Files:
Developers:
libraryDependencies += "io.github.tilumi" %% "azure-eventhubs-spark" % "v2.3.hack-for-spark-aggregator.1"
ivy"io.github.tilumi::azure-eventhubs-spark:v2.3.hack-for-spark-aggregator.1"
//> using dep "io.github.tilumi::azure-eventhubs-spark:v2.3.hack-for-spark-aggregator.1"
import $ivy.`io.github.tilumi::azure-eventhubs-spark:v2.3.hack-for-spark-aggregator.1`
<dependency> <groupId>io.github.tilumi</groupId> <artifactId>azure-eventhubs-spark_2.11</artifactId> <version>v2.3.hack-for-spark-aggregator.1</version> </dependency>
compile group: 'io.github.tilumi', name: 'azure-eventhubs-spark_2.11', version: 'v2.3.hack-for-spark-aggregator.1'
