Home
about banner

Apache Flink





Apache Flink
Day1:::
  • Module 1:::
    • Introduction of Big Data
    • Hadoop Architecture
    • YARN MR Application Execution Flow,
    • YARN Workflow
    • Spark Introduction
    • Architecture and Ues Cases.
    • Flink Introduction and USE CASES.
    • Introduction to Flink
    • Flink Introduction
    • Batch Processing Vs Stream Processing
    • Hadoop Vs Streaming Engines (Spark & Flink)
    • Spark Vs Flink
    • Flink Architecture/Ecosystem
    • Flink's programming model | Flow of a Flink program
    • Installing Flink
  • Module 2:::
    • Transformation operations of DataSet API
    • Default Code structure of a Flink Program
    • WordCount using Map, Flatmap, Filter, groupby
    • Joins - Inner join
    • Joins - Left, Right & Full Outer Join
    • Join Hints for Optimization (Exclusive feature)
Day 2:::
  • Module 1:::
    • DataStream API Operations
    • Data Sources & Sinks of Datastream API
    • First program using Datastream API
    • Reduce Operation
    • Fold Operation
    • Aggregation Operations in Flink
    • Split Operation
  • Module 2:::
    • Windows in Flink
    • Introduction to Windowing
    • Window Assigners
    • Various Time Notions of Windows in Flink
    • Tumbling Windows Implementation
    • Sliding Windows Implementation
    • Session Windows Implementation
    • Global Windows Implementation
Day 3:::
  • Module 1
    • Triggers & Evictors
    • Triggers in Windows
    • Evictors for Windows
    • Watermarks and Late elements
    • Watermarks, Late Elements & Allowed Lateness
    • How to generate Watermarks
  • Module 2:::
    • State, Checkpointing and Fault tolerance
    • What is a State in Flink
    • Checkpointing and Barrier Snapshoting
    • Incremental Checkpointing (New Feature)
    • Types of States
    • Value State Implementation
    • List State Implementation
    • Processing..
    • Reducing State Implementation
    • Managed Operator State Implementation
    • Implement Checkpointing in a Flink Program
    • The Broadcast State Implementation
Day 4:::
  • Module 1:::
    • Interacting with Real-Time Data
    • Getting Twitter data using its APIs
    • Adding Kafka to Flink as a Data source
    • Solve Real-Time Case studies using Flink
    • Twitter data analysis using Flink
    • Bank Real-Time Fraud detection
    • Stock Real-Time Data Processing
    • Table & Sql API | Relational APIs of Flink
    • Introduction to Table & Sql API
    • How to register a Table in Relational APIs
    • Writing Queries in Table & Sql API
  • Module 2:::
    • Gelly API for Graph Processing
    • What is a Graph
    • Calculate Friends of Friends of a Person using GELLY Api