Description



Understand the technical architecture along with installation and configuration of Spring Cloud Data Flow Applications.



Create basic to advanced Streaming applications like time logger to TensorFlow Image Detection Stream Flow.



You will learn the following as part of this course.



   Architecture of Spring Cloud Data Flow

   Components of Spring Cloud Data Flow like Skipper Server, Spring Cloud Data Flow Server, Data Flow Shell

   Using Data Flow Shell and Domain Specific Language (DSL)

   Configuring and usage of message brokers like RabbitMQ, Kafka

   Installation and configuration of Spring Cloud Data Flow Ecosystem in Amazon Web Service (AWS) EC2 Instances

   Configuring Grafana Dashboard for Stream visualization

   Configuration of Source, Sink and Processor

   Creating custom Source, Sink and Processor application

   Coding using Spring Tool Suite (STS) for custom code development

   Working with Spring Data Flow WebUI and analyzing logs on runtimes



This course is designed to cover all aspects of Spring Cloud Data Flow from basic installation to configuration in Docker as well as creating all type of Streaming applications like ETL, import/export, Predictive Analytics, Streaming Event processing etc.,



Few working examples/usecases are covered to have better understanding like



   Data extracting and interaction with JDBC database

   Extracting Twitter Data (Tweets) from Twitter

   Sentiment analysis, Language Analysis and HashTag Analysis on Tweets from Twitter

   Object Detection/Prediction using TensorFlow processor

   Pose Prediction using TensorFlow Processor



Who this course is for:



   Those who wants to create microservice based Streaming and Batch data processing

   Those who wants to create Cloud Native ETL Applications



Requirements



   Basics on Spring Framework, Spring Boot and Microservices



Last Updated 7/2019