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Apache Spark 3 - Beyond Basics and Cracking Job Interviews

Learning Journal via YouTube

Overview

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This course is designed to teach Apache Spark 3 and how to ace job interviews related to it. It covers topics related to Spark cluster and runtime architecture to help students understand the internal workings of the framework. It also provides hands-on aims related to Spark submit and deploy modes. There is a greater focus on Spark jobs which consists of the analysis of Stages, Shuffles, Tasks and Slots. This course also provides knowledge about a range of techniques such as Spark SQL Engine and Query Planning, Memory Allocation, Memory Management, Adaptive Query Execution, Dynamic Join Optimization, Data Skew and Partition Pruning, Data Caching, Repartition and Coalesce. It includes several practical exercises and quizzes and their solutions with video, as well as topics such as broadcast variables, accumulators and dynamic resource allocation.

Syllabus

Apache Spark 3 - Beyond Basics and Cracking Job Interviews | Course Introduction.
Spark Cluster and Runtime Architecture.
Spark Submit and Some Important Options.
Deploy Modes - Client and Cluster mode.
Spark Jobs - Stage, Shuffle, Task, Slots.
Spark SQL Engine and Query Planning.
Lets Practice - Quiz 1 Solution Video.
Lets Practice - Quiz 2 Solution Video.
Spark Memory Allocation.
Spark Memory Management.
Spark Adaptive Query Execution.
Spark AQE Dynamic Join Optimization.
Handling Data Skew in Spark Joins.
Spark Dynamic Partition Pruning.
Data Caching in Spark.
Repartition and Coalesce.
Dataframe Hints.
Broadcast Variables.
Accumulators.
Speculative Execution.
Dynamic Resource Allocation.
Spark Schedulers.
Lets Practice Quiz 3 Solution Video.
Lets Practice Quiz 4 Solution Video.

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Learning Journal

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