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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 3
Data Processing and Analysis with Pyspark
Summary
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Concept Check
What is a key benefit of using Pyspark for data processing and analysis?
Single-threaded data analysis
Optimized parallel processing
Interactive data querying
Sequential data processing
Which programming language is commonly used with Pyspark for data analysis?
R
Scala
Python
Java
What is an example of a data source that Pyspark can directly read from?
JSON files
SQLite database
PDF documents
Excel spreadsheets
What does Pyspark use to efficiently distribute data processing tasks?
Pandas dataframes
Python lists
SQL queries
Resilient Distributed Datasets (RDDs)
What is the primary purpose of using Pyspark for big data processing tasks?
Ease of debugging
Graphical data visualization
Scalability and performance
Compatibility with MATLAB
Check Answer
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Working with DataFrames in Pyspark
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Machine Learning with Pyspark