Data Stream Development via Spark, Kafka and Spring Boot
- 4.3
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Brief Introduction
Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipelineDescription
Today, organizations have a difficult time working with huge numbers of datasets. In addition, data processing and analyzing need to be done in real time to gain insights. This is where data streaming comes in. As big data is no longer a niche topic, having the skillset to architect and develop robust data streaming pipelines is a must for all developers. In addition, they also need to think of the entire pipeline, including the trade-offs for every tier.
This course starts by explaining the blueprint architecture for developing a completely functional data streaming pipeline and installing the technologies used. With the help of live coding sessions, you will get hands-on with architecting every tier of the pipeline. You will also handle specific issues encountered working with streaming data. You will input a live data stream of Meetup RSVPs that will be analyzed and displayed via Google Maps.
By the end of the course, you will have built an efficient data streaming pipeline and will be able to analyze its various tiers, ensuring a continuous flow of data.
About the Author
Anghel Leonard is currently a Java chief architect. He is a member of the Java EE Guardians with 20+ years’ experience. He has spent most of his career architecting distributed systems. He is also the author of several books, a speaker, and a big fan of working with data.
Requirements
- Requirements
- Having knowledge of the Spring framework will be an added benefit.
Packt Publishing
4.3 Raiting
Data Stream Development via Spark, Kafka and Spring Boot
- 4.3
-
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Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipeline Today, organizations have a difficult time working with huge numbers of datasets....
