Free Download Windowing and Join Operations on Streaming Data with Apache Spark on Databricks
Janani Ravi | Duration: 2:02 h | Video: H264 1280x720 | Audio: AAC 48 kHz 2ch | 242 MB | Language: English
This course will teach you how to leverage windowing, watermarking, and join operations on streaming data in Spark for your specific use cases.
Structured Streaming in Apache Spark treats real-time data as a table that is being constantly appended. In such a stream processing model the burden of stream processing shifts from the user to the system, making it very easy and intuitive to process streaming data with Spark. Apache Spark supports a range of windowing and join operations on streaming data using processing time and event time.
In this course, Windowing and Join Operations on Streaming Data with Apache Spark on Databricks, you will learn the difference between stateless operations that operate on a single streaming entity and stateful operations that operate on multiple entities accumulated in a stream. Then, you will explore the different kinds of windows supported by Apache Spark which includes tumbling windows, sliding windows, and global windows.