Applying the Lambda Architecture with Spark, Kafka, and Cassandra

This course introduces how to build robust, scalable, real-time big data systems using a variety of Apache Spark's APIs, including the Streaming, DataFrame, SQL, and DataSources APIs, integrated with Apache Kafka, HDFS and Apache Cassandra.
Course info
Rating
(110)
Level
Beginner
Updated
Nov 15, 2016
Duration
6h 4m
Table of contents
Course Overview
A Modern Big Data Architecture
Batch Layer with Apache Spark
Speed Layer with Spark Streaming
Advanced Streaming Operations
Streaming Ingest with Kafka and Spark Streaming
Persisting with Cassandra
Description
Course info
Rating
(110)
Level
Beginner
Updated
Nov 15, 2016
Duration
6h 4m
Description

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.

About the author
About the author

Ahmad is a Data Architect specializing in the implementation of high-performance data warehouses and BI systems and enjoys speaking at various user groups and conferences.

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