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Learn By Example: Spark Streaming 2.x
You, This Course and Us
You, This Course and Us (2:09)
Course Materials
Streaming API in Spark 2.x
Overview (2:59)
Resilient Distributed Datasets (RDDs) With Streaming Data (4:46)
Streaming Architecture (10:06)
DStreams In Spark 1.x (4:05)
Structured Streaming in Spark 2.x (5:15)
Installation and setup (5:10)
What Are Continuous Applications? (6:57)
Triggers And Output Modes (8:54)
Netcat (7:57)
Streaming Pipelines
Append Mode (6:50)
Complete Mode (3:48)
Average Aggregations (3:21)
SQL Queries (4:46)
Timestamps (3:03)
Groupby Timestamp (2:38)
Window Transformations (4:27)
Tumbling And Sliding Windows (3:37)
Event, Ingestion And Processing Time (6:13)
Windowing (5:43)
Watermarks (7:12)
Twitter Keys And Access Tokens (5:35)
Twitter Streaming (4:25)
Count Hashtags (4:26)
Count Hashtags: Windows (3:32)
Joins (2:44)
Aggregate Joins (2:23)
Aggregate Score By Enrollment (2:07)
Windowed Joins (2:56)
Spark + Kafka
Kafka (4:30)
Producer-Consumer (4:06)
Hashtag Producer (4:39)
German to English Conversion (3:44)
Tweet Producer (3:08)
Summary (2:12)
What Are Continuous Applications?
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