Learn By Example : Apache Flink

Learn By Example : Apache Flink, available at $34.99, has an average rating of 4.36, with 41 lectures, based on 348 reviews, and has 4578 subscribers.

You will learn about Use the DataStream API for transforming streaming data Use the DataSet API for batch processing Apply window operations on Streaming data Use Flink-ML for Machine Learning Use Gelly for Graph processing This course is ideal for individuals who are Yep! Engineers looking to set up end-to-end data processing pipelines that react to changes in real time or Yep! Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing It is particularly useful for Yep! Engineers looking to set up end-to-end data processing pipelines that react to changes in real time or Yep! Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing.

Enroll now: Learn By Example : Apache Flink

Summary

Title: Learn By Example : Apache Flink

Price: $34.99

Average Rating: 4.36

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Use the DataStream API for transforming streaming data
Use the DataSet API for batch processing
Apply window operations on Streaming data
Use Flink-ML for Machine Learning
Use Gelly for Graph processing

Who Should Attend

Yep! Engineers looking to set up end-to-end data processing pipelines that react to changes in real time
Yep! Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing

Target Audiences

Yep! Engineers looking to set up end-to-end data processing pipelines that react to changes in real time
Yep! Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing

Flink is a stream processing technology with added capability to do lots of other things like batch processing, graph algorithms, machine learning etc. 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.�

This course has 30 Solved Examples on building Flink Applications for both Streaming and Batch Processing

What’s covered?

1) Transformations in the DataStream API :�filter, map, flatMap and reduce

2) Operations on multiple streams : union, cogroup, connect, comap, join and iterate

3) Window operations :�Tumbling, Sliding, Count and Session windows; the notion of time and how to implement custom Window functions�

4) Managing fault-tolerance�with State and Checkpointing�

5) Transformations in the DataSet API :�filter, map, reduce, reduceGroup

6) Applying ML algorithms on the fly using Flink-ML

7) Representing Graph data using Gelly

Course Curriculum

Chapter 1: Introduction

Lecture 1: You, This Course and Us

Chapter 2: Flink's Stream Processing Architecture

Lecture 1: What is stream processing?

Lecture 2: Stream processing vs Batch processing

Lecture 3: Requirements of a Streaming Architecture

Lecture 4: Stream processing with Apache Flink

Chapter 3: Getting Started with Flink

Lecture 1: Installing Flink

Lecture 2: Setting up your Flink project with Maven

Chapter 4: Hello World!

Lecture 1: Data Representation and Programming Model

Lecture 2: Example 1: Writing a Flink program

Chapter 5: Transformations using the DataStream API

Lecture 1: Example 2: The Filter operation

Lecture 2: Example 3: The Map operation

Lecture 3: Example 4: The FlatMap operation

Lecture 4: Stateless and Stateful Transformations

Lecture 5: Keyed Streams

Lecture 6: Example 5: Creating a stream of Tuples

Lecture 7: Example 6: Transformations on Keyed Streams

Lecture 8: Example 7: Number aggregations

Lecture 9: Example 8: The Reduce Operation

Chapter 6: Window Operations

Lecture 1: Windows Transformation

Lecture 2: Example 9 and 10: Keyed vs NonKeyed, Sliding and Tumbling Windows

Lecture 3: Example 11: Count Windows

Lecture 4: Example 12: Session Windows

Lecture 5: Understanding the Window API

Lecture 6: Example 13: Implementing a Custom Window Function

Lecture 7: Example 14: Changing the time characteristic

Lecture 8: Example 15: Twitter Streaming Window

Chapter 7: Custom Sources

Lecture 1: Example 16: Custom Sources

Chapter 8: State and Checkpointing

Lecture 1: Example 17: Value State

Lecture 2: Example 18: List State

Lecture 3: Example 19: Reducing State

Lecture 4: Example 20: Checkpointing and Restart strategies

Chapter 9: Operations on Multiple Streams

Lecture 1: Example 21: Unions

Lecture 2: Example 22: Joining Streams

Lecture 3: Example 23: coGroup

Lecture 4: Example 24: coMap

Lecture 5: Example 25: Iterate

Lecture 6: Example 26: Split

Chapter 10: Transformations in the DataSet API

Lecture 1: Example 27: Applying Transformations on DataSets

Chapter 11: Graph representation with Gelly

Lecture 1: Example 28: Representing Graph data

Chapter 12: Machine Learning with Flink ML

Lecture 1: Example 29: Scala Hello World

Lecture 2: Example 30: Recommendations with Alternating Least Squares

Instructors

Loony Corn
An ex-Google, Stanford and Flipkart team

Rating Distribution

1 stars: 14 votes
2 stars: 19 votes
3 stars: 75 votes
4 stars: 124 votes
5 stars: 116 votes

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