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.
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Architecture of Spring Cloud Data Flow
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Components of Spring Cloud Data Flow like Skipper Server, Spring Cloud Data Flow Server, Data Flow Shell
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Using Data Flow Shell and Domain Specific Language (DSL)
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Configuring and usage of message brokers like RabbitMQ, Kafka
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Installation and configuration of Spring Cloud Data Flow Ecosystem in Amazon Web Service (AWS) EC2 Instances
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Configuring Grafana Dashboard for Stream visualization
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Configuration of Source, Sink and Processor
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Creating custom Source, Sink and Processor application
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Coding using Spring Tool Suite (STS) for custom code development
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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
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Data extracting and interaction with JDBC database
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Extracting Twitter Data (Tweets) from Twitter
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Sentiment analysis, Language Analysis and HashTag Analysis on Tweets from Twitter
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Object Detection/Prediction using TensorFlow processor
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Pose Prediction using TensorFlow Processor
