The training equips developers with the knowledge and skills necessary to enhance the performance of their Apache Spark applications. Participants will get knowledge of the best practices for monitoring Spark applications as well as how to recognize typical causes of poor performance in Spark applications.
Learn Apache Spark's architecture, job execution, and performance-enhancing methods like lazy execution and pipelining work.
Analyze the performance traits of fundamental data structures like RDDs and DataFrames.
Choose the file types that will run your applications most effectively.
Determine and fix performance issues brought on by data skew.
Use join improvements, bucketing, and partitioning to boost SparkSQL's speed.
Recognize the performance overhead of RDDs, DataFrames, and user-defined functions based on Python.
Utilize caching for improved application performance.
Recognize the operation of the Tungsten and Catalyst optimizers.
Learn how Workload XM may be used to proactively monitor and troubleshoot Spark application performance.
Discover the improvements in performance brought by the Adaptive Query Execution engine as well as other new features in Spark 3.0.
Software developers
Engineers
Data scientists who have experience developing Spark applications and want to learn how to improve the performance of their code.
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