Structured Streaming in Apache Spark 2

Unlock the power of real-time data processing with Structured Streaming in Apache Spark 2. This course dives into leveraging Spark's distributed analytics engine for analyzing continuously updated data and reacting to changes as they happen.

Learn how Spark 2.x data frames handle infinite data, seamlessly unifying batch processing and streaming applications. You'll master the tabular data frame API to work with streaming, unbounded datasets using familiar APIs.

Key topics covered include:

• Understanding Structured Streaming: Explore the architecture and advantages of structured streaming compared to traditional streaming approaches. Discover how it enables real-time data reactions.
• Triggers & Output Modes: Learn to define triggers for evaluating streaming results and configure various output modes to write data to files or display it on the screen.
• Building Streaming Pipelines: Develop robust pipelines using event time aggregations, grouping, and windowing functions. Learn to perform powerful join operations between batch and streaming data.
• Real-World Application: Twitter Stream Analysis: Work with real Twitter streams to perform insightful analysis on trending hashtags.
• Spark Streaming & Kafka Integration: Discover how to integrate Spark stream processing with Kafka, a distributed publisher-subscriber system, by ingesting Twitter data from a Kafka producer and processing it with Spark Streaming.

By the end of this course, you'll be proficient in performing advanced stream data analysis using Spark's high-level structured streaming API. Gain the skills to build scalable and efficient data processing applications for a variety of real-time use cases.