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Data Engineering
Unit 1
Azure Data Engineer
Azure Data Factory
Azure SQL Database
Azure Synapse Analytics
Azure Cosmos DB
Azure Data Lake Storage
Unit 2
Pyspark
Introduction to Pyspark
Working with DataFrames in Pyspark
Data Processing and Analysis with Pyspark
Machine Learning with Pyspark
Optimizing Pyspark Performance
Unit 3
SQL
Introduction to SQL
Data Retrieval with SQL
Data Manipulation with SQL
Unit 2 • Chapter 5
Optimizing Pyspark Performance
Summary
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Concept Check
What is a common method for optimizing Pyspark performance?
Running tasks sequentially
Partitioning data
Shuffling data frequently
Ignoring data skew
How can we improve the efficiency of Pyspark jobs?
Ignoring lazy evaluation
Using unoptimized transformations
Running jobs on small clusters
Caching intermediate results
What is an important consideration for Pyspark performance tuning?
Choosing the right hardware configuration
Selecting arbitrary partition sizes
Not monitoring job progress
Avoiding parallelism
Which parameter can impact Pyspark job execution times significantly?
Using default shuffle partitions
Optimizing cluster disk space
Number of partitions
Amount of available memory
Why is it crucial to optimize data skew in Pyspark applications?
To introduce more shuffle operations
To reduce cluster memory usage
To increase data redundancy
To avoid bottleneck and uneven workload
Check Answer
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Machine Learning with Pyspark