Sensors, IoT devices, social networks, and online transactions are all generating data that needs to be monitored constantly and acted on quickly. As a result, the need for large-scale, real-time stream processing is more evident now than ever before.
With Azure Databricks running on top of Spark, Spark Streaming enables data scientists and data engineers with powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. Azure Databricks readily integrates with a wide variety of popular data sources, including HDFS, Flume, Kafka, and Twitter.
Read about it in the Azure blog.