Learn By Example: Apache Flink

This program has Thirty Solved Examples for developing both batch processing and streaming apps

Self-Paced

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Java

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Intermediate

Difficulty

3 Hours

Course Duration

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This program has Thirty Solved Examples for developing both batch processing and streaming apps

Course Information

About This Course:

Flink is a stream processing system with an added ability to do many other things, like machine learning, graph algorithms, batch processing, etc. Using Flink you can create apps that allow you to be extremely sensitive to the latest data, such as tracking spikes in payment gateway failure or triggering live stock price movements.

This program has Thirty Solved Examples for developing both batch processing and streaming apps

Course Objective:

· Multiple-stream operations: cogroup, union, comap, connect, iterate and join

· DataStream API Transformations: Map, Filter, Reduce and FlatMap

· Window operations: Tumbling, Sliding, Session and Count windows; time notion and how custom Window functions are implemented

· DataSet API transformations: reduce, map, filter, reduce the Group

· Managing System and Checkpointing fault tolerance

· Using Gelly to represent Graph data

· Usage of Flink-ML to apply ML algorithms on the fly

Audience:

· People who are familiar with Batch processing techniques such as Hadoop want to know more about Stream processing

· Engineers who are currently looking to configure end-to-end data processing pipelines that adapt to real-time changes

Prerequisite:

· You can install a Java IDE, such as IntelliJ Idea

· Java programming expertise and familiarity with the use of Java frameworks

· Creating Jars with Maven, debugging and compiling Java code

Outline

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Brand Java
Subjects App Development, Big Data
Lab Access No
Learning Style Self-Paced Learning
Learning Type Course
Difficulty Intermediate
Course Duration 3 Hours
Language English
VPA Discount VPA Discount

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Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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