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Flink is a stream processing technology with added capability to perform bonus functions like batch processing, graph algorithms, machine learning, and more. Using Flink you can build applications which need you to be highly responsive to the latest data such as monitoring spikes in payment gateway failures, or triggering trades based on live stock price movements.
- Access 42 lectures & 4 hours of content 24/7
- Explore transformations in the DataStream API & the DataSet API
- Discuss operations on multiple streams
- Discover window operations
- Manage fault-tolerance w/ State & Checkpointing
- Apply machine learning algorithms on the fly using Flink-ML
- Represent graphical data using Gelly
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Terms
- Instant digital redemption
Course Outline
- 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)
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