Tuning Apache Spark: Powerful Big Data Processing Recipes
About this course
Video Learning Path OverviewA Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.Today, organizations have a difficult time working with large datasets. In addition, big data processing and analyzing need to be done in real time to gain valuable insights quickly. This is where data streaming and Spark come in.In this well thought out Learning Path, you will not only learn how to work with Spark to solve the problem of analyzing massive amounts of data for your organization, but you’ll also learn how to tune it for performance. Beginning with a step by step approach, you’ll get comfortable in using Spark and will learn how to implement some practical and proven techniques to improve particular aspects of programming and administration in Apache Spark. You’ll be able to perform tasks and get the best out of your databases much faster.Moving further and accelerating the pace a bit, You’ll learn some of the lesser known techniques to squeeze the best out of Spark and then you’ll learn to overcome several problems you might come across when working with Spark, without having to break a sweat. The simple and practical solutions provided will get you back in action in no time at all!By the end of the course, you will be well versed in using Spark in your day to day projects.Key FeaturesFrom blueprint architecture to complete code solution, this course treats every important aspect involved in architecting and developing a data streaming pipelineTest Spark jobs using the unit, integration, and end-to-end techniques to make your data pipeline robust and bulletproof.Solve several painful issues like slow-running jobs that affect the performance of your applicati
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What you'll learn
- how to work with Apache Spark
- tuning Spark for performance
- implementing data streaming
- testing Spark jobs using various techniques
- overcoming common Spark problems
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