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Databricks data engineer certification
·
Module 5
Transforming Data with PySpark DataFrames
1
Loading Data into DataFrames with PySpark
Read data from files (CSV, JSON, Parquet) into DataFrames using PySpark.
2
Manipulating Columns in PySpark DataFrames
Select, rename, and drop columns in a DataFrame using the PySpark API.
Filtering DataFrame Rows
Filter rows in a DataFrame based on single or multiple conditions.
4
Transforming DataFrames: Adding, Modifying, and Ordering Columns
Modify DataFrames by adding or changing columns using `withColumn` and sorting with `orderBy`.
5
Grouping and Aggregating Data
Summarize data by applying aggregate functions after a `groupBy` operation.
6
PySpark DataFrame Joins
Join DataFrames using different join types in PySpark.
Writing DataFrames to Delta Tables
Write DataFrames to Delta tables using various save modes (append, overwrite).
8
Querying DataFrames with Spark SQL and Temporary Views
Execute SQL queries on DataFrames using `spark.sql()` and register DataFrames as temporary views.
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Transforming Data with Spark SQL
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Incremental Data Ingestion