What you'll learn:
- Architecture of Spring Cloud Data Flow
- Skipper Server, Spring Data Flow Server, Spring Data Flow Shell installation and configuration
- Microservice based Streaming and Batch data processing
- Examples with ETL, import/export, even streaming and predictive analytics
- Examples with Twitter Sentiment Analysis, TensorFlow Object Detection
- Install and Configure Spring Cloud Data Flow Ecosystem in Docker
- Configure Grafana Dashboard for Stream Visualization
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
