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Learn by Example: Apache Flink
Introduction
You, This Course and Us (2:05)
Flink's Stream Processing Architecture
What is stream processing? (3:47)
Stream processing vs Batch processing (6:31)
Requirements of a Streaming Architecture (12:14)
Stream processing with Apache Flink (6:17)
Getting Started with Flink
Installing Flink (6:34)
Setting up your Flink project with Maven (1:44)
Hello World!
Data Representation and Programming Model (4:36)
Example 1: Writing a Flink program (9:31)
Transformations using the DataStream API
Example 2: The Filter operation (6:04)
Example 3: The Map operation (5:10)
Example 4: The FlatMap operation (3:31)
Stateless and Stateful Transformations (2:47)
Keyed Streams (1:42)
Example 5: Creating a Stream with Tuples (2:54)
Example 6: Transformations on Keyed Streams (2:52)
Example 7: Number Aggregations (2:18)
Example 8: The Reduce operation (6:03)
Window operations
Window Transformations (3:23)
Example 9 and 10: Keyed vs NonKeyed, Sliding and Tumbling Windows (5:48)
Example 11: Count Windows (5:25)
Example 12: Session Windows (1:32)
Understanding the Window API (3:22)
Example 13: Implementing a Custom Window Function (4:48)
Example 14: Changing the time characteristic (7:30)
Example 15: Twitter Streaming Window (2:38)
Custom Sources
Example 16: Custom Sources (3:23)
State and Checkpointing
Example 17: Value State (5:34)
Example 18: List State (3:11)
Example 19: Reducing State (2:44)
Example 20: Checkpointing and Restart strategies (3:36)
Operations on Multiple Streams
Example 21: Unions (1:58)
Example 22: Joining Streams (3:43)
Example 23: coGroup (4:42)
Example 24: coMap (2:26)
Example 25: Iterate (4:01)
Example 26: Split (2:06)
Transformations in the DataSet API
Example 27: Applying Transformations on DataSets (6:28)
Graph representation with Gelly
Example 28: Representing Graph data (3:42)
Machine Learning with Flink ML
Example 29: Scala Hello World (2:33)
Example 30: Recommendations with Alternating Least Squares (5:21)
Stream processing vs Batch processing
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