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Applying the Lambda Architecture with Spark Kafka and Cassandra

Applying the Lambda Architecture with Spark, Kafka, and Cassandra

with Ahmad Alkilani


05_07-Stateful Transformations_ mapWithState



This course aims to get beyond all the hype in the big data world and focus on what really works for building robust, highly-scalable batch and real-time systems. In this course, Applying the Lambda Architecture with Spark, Kafka, and Cassandra, you'll string together different technologies that fit well and have been designed by some of the companies with the most demanding data requirements (such as Facebook, Twitter, and LinkedIn) to companies that are leading the way in the design of data processing frameworks, like Apache Spark, which plays an integral role throughout this course. You'll look at each individual component and work out details about their architecture that make them good fits for building a system based on the Lambda Architecture. You'll continue to build out a full application from scratch, starting with a small application that simulates the production of data in a stream, all the way to addressing global state, non-associative calculations, application upgrades and restarts, and finally presenting real-time and batch views in Cassandra. When you're finished with this course, you'll be ready to hit the ground running with these technologies to build better data systems than ever.

      
Course Contents
01. Course Overview 02. A Modern Big Data Architecture 03. Batch Layer with Apache Spark 04. Speed Layer with Spark Streaming 05. Advanced Streaming Operations 06. Streaming Ingest with Kafka and Spark Streaming 07. Persisting with Cassandra 08. Exercise files

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