| Sumario: | Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipeline About This Video From blueprint architecture to complete code solution, this course treats every important aspect involved in architecting and developing a data streaming pipeline Select the right tools and frameworks and follow the best approaches to designing your data streaming framework Build an end-to-end data streaming pipeline from a real data stream (Meetup RSVPs) and expose the analyzed data in browsers via Google Maps In Detail 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. All the code and supporting files for this course are available at https://github.com/PacktPublishing/-Data-Stream-Development-with-Apache-Spark-Kafka-and-Spring-Boot
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