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Real-Time Analytics using NiFi and Spark

  /    /  Real-Time Analytics using NiFi and Spark

Real-Time Analytics using NiFi and Spark

Categories:
Big Data
Reviews:

The interactive virtual workshop is focussed on Building Data Flows, Performing Streaming Ingestion and Streaming Processing using NiFi and Spark Streaming. This program covers below aspects in NiFi and Spark –

  • Fundamentals, 
  • Limitations, 
  • Architectures, 
  • Hands-on Exercises

Spark API perspective below areas will be covered –

  • Micro-batch Processing 
  • Structured Streaming Processing

Hands-on exercises related to Spark will be in Python.

  • Application Developers
  • DevOps Engineers
  • Architects
  • System Engineers
  • Technical Managers
Introduction to Streaming
  • Streaming Overview
  • Streaming Use cases
  • Streaming Frameworks 
    • NiFi vs Kafka
    • Kafka Streams vs Spark Streaming
  • Challenges in Streaming 
  • Which one to choose when?
Introduction to NiFi
  • NiFi Overview
  • Limitations of NiFi
  • NiFi Terms
  • Hands-on: Setting up NiFi 
  • Demo: Working with NiFi User Interface
NiFi Architecture
  • Logical Architecture of NiFi
  • Physical Architecture of NiFi
  • Roles and Responsibilities of various components
  • Hands-on exercise 
Working with NiFi Flows
  • Anatomy of FlowFile
  • Key NiFi Processors
  • Configuring Queues 
  • NiFi Templates
  • Hands-on exercise
Delving Deeper in NiFi
  • NiFi Registry
  • NiFi Expression Language
  • Controller Service
  • Hands-on exercise
Integrating NiFi with Kafka
  • Role of Kafka
  • Kafka Terms
  • Kafka Processors
  • Hands-on exercise
Spark Streaming Overview
  • Spark Logical Architecture
  • Various APIs of Spark Streamings
  • DStream API vs Structured Streaming
  • Terms and Concepts
Streaming ETL using Spark
  • Processing streaming data using Spark 
  • Various Types of Windowing
  • Watermarking and Triggers Concept
  • Stateless Streaming
  • Hands-on exercise: Stateless Streaming
  • Stateful Streaming
  • Hands-on exercise: Stateful Streaming
  • Structured Streaming
  • Hands-on exercise: Structured Streaming
Integrations and Flows using Streaming Concepts
  • Structured Streaming Integrations 
  • Kafka Key Configurations
  • Hands-on: Integrating Kafka with Spark Streaming
  • Hands-on Integrate Spark Streaming with HBase
  • Hands-on: Build End to End Flow from Twitter to HBase
  • Basic Knowledge of Big Data Technologies as covered in Big Data Crash Course or equivalent knowledge
  • Basic knowledge of Python

Upon completion of this course, you should be able to:

  • Understand and Gain Hands-on exposure regarding 
    • Key Streaming Frameworks
    • Pros and Cons of NiFi, Spark and Kafka
    • Spark and NiFi Fundamentals & Architecture, 
    • Integration of NiFi and Spark Streaming via Apache Kafka
  • Spark Micro-batch processing 
  • Structured Streaming Processing

Course Information

Duration

3 Days

Mode of Delivery

Instructor led/Virtual

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