Structured Streaming With Azure DataBricks

In this blog, we are going to cover Structured Streaming with Azure Databricks, Streaming concepts, Event Hubs, and Spark Structured Streaming and Perform stream processing using structured streaming.

Apache Spark Structured Streaming is a quick, versatile, and fault-tolerant stream handling API. You can utilize it to perform analytics on your streaming information in real-time.

With Structured Streaming, you can utilize SQL queries to handle streaming information similar to how you would deal with static data. The API consistently augments and updates the final data.

Streaming Concepts

Stream handling is the place where you persistently fuse new data into Data Lake storage and process the results. The streaming data comes in quicker than it tends to be consumed while utilizing conventional traditional batch-related processing techniques. A stream of data is treated as a table to which data is continuously appended. Instances of such data incorporate bank card transactions, Internet of Things (IoT) gadgets data, and computer gameplay events.

A streaming system consists of the following:

-Stream processing using Structured Streaming, forEach sink, memory sinks, etc.
-Input sources such as Azure Event Hubs, Kafka, IoT Hub, files on a distributed system, or TCP-IP sockets.

Want to know more about Structured Streaming With Azure DataBricks
Read the blog post at to learn more.

Topics weโ€™ll Cover:

Azure Databricks Structured Streaming
Streaming Concepts
Event Hubs and Spark Structured Streaming
Perform stream processing using structured streaming.

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