Flink Training
Learn Flink Thats Bring Big Data Processing Solutions With Best Practices
THE GOAL OF COURSE
The purpose of the Flink Training Program is to enable participants to consolidate their knowledge in the their minds with experience sharing, example implementations and case studies, while providing detailed information on key concepts and techniques on Flink
WHAT YOU WILL LEARN
- Learn the terminology and basic concepts behind Flink Architecture and its components, selection criteria and best solution designs,
- Have deep knowledge on the Flink Ecosystem,
- Understand how you should prepare your Flink architecture to production,
- Specialize in the most appropriate cluster configurations and integrations with the hardware and systems within the data center,
- Learn how to work with DataSet, DataStream and DataTable API’s in detail,
- Develop technical knowledge and competencies with workshops,
- Make a significant contribution to your development in Big Data,
- Be able to find answers to your questions at the points where your head is installed while developing after the program
WHO BENEFITS
- Employees who are doing or planning to make a career in the Big Data place
- Leading teams in technology / IT functions
- Leaders responsible for Digital Transformation.
- Data Scientists and Data Analysts
- New Graduates and University Students
Gain In Depth Practical Knowledge on Flink Next-Generation Scalable Stream and Batch Processing Engine
Theories and practices you will see throughout the program will answer your questions on Flink Ecosystem, also you will learn key techniques to develop the right strategy while implementing your projects
COURSE OUTLINE
- Module-1: Introduction to Flink
- The History of Flink
- Flink Architecture and Design Principles
- Flink Terminology and Its Components
- JobManager and TaskManager Operations
- Understanding Workers, Slots and Resources
- Flink and Spark Comparison
- Module-2: Flink Components
- Apache Flink APIs
- Apache Flink Libraries
- Data Flow in Flink
- Distributed Data Flow at Runtime with Flink
- Fault Tolerance in Flink
- Flink Single Node Installation and Configuration
- Flink UI Web Interface
- Module-3 DataStream API for Streaming Data
- Flink DataStream API Framework
- Different DataStream Transformations
- File, Socket, Collection and Custom Data Sources
- The Role of DataSink in the DataStream API
- Iterations in DataStream APIs
- DataStream Execution Parameters
- Controlling Fault Tolerance and Delay
- Lab: DataStream API Workshop
- Module-4 DataSet API for Static Datasets
- Flink DataSet API Framework
- Different DataSet Transformations
- The Role of DataSink in the DataSet API
- Iterations in DataSet APIs
- Managing Data Objects in Functions
- (Object Reuse Disabled / Enabled)
- Lab: DataSet API Workshop
- Module-5 Table API and Working With SQL
- Saving Tables in Flink
- Table Access and Miscellaneous Table API Operators
- Running SQL
- Streaming Tables
- Writing Flink Tables to External Sinks
- Lab: Table API and SQL Workshop Studies
- Module-6 Flink Integrations and Best Practices
- Recognizing Flink Libraries
- Flink CEP – Complex Event Handling Library
- Flink Machine Learning Library
- Flink Gelly – Graphics Processing Library
- Flink Hadoop Integration
- Real Time Data Processing with Kafka
- Debugging and Troubleshooting Flink
- Production Setup and Performance Tips
- Lab: You will learn on the sample data set by applying lots of examples on Flink Components