Delta tables are a new feature in Apache Spark that allow you to store large datasets as an efficient and scalable set of files on disk. Delta tables support fast, ACID transactions and provide efficient data management and access for data lakes and data pipelines.
To create a Delta table from a PySpark DataFrame, you can use the write.format() function with the "delta" option. For example:
df.write.format("delta").save("/path/to/delta/table")
This will create a Delta table at the specified path, and write the data from the DataFrame to the table.
You can also specify additional options when creating a Delta table. For example, you can use the mode option to specify whether to append the data to the table, overwrite the table, or throw an error if the table already exists. For example:
df.write.format("delta").mode("overwrite").save("/path/to/delta/table")
You can also use the partitionBy option to specify a column or set of columns to use as partition columns in the Delta table. For example:
df.write.format("delta").partitionBy("col1", "col2").save("/path/to/delta/table")
Write method has these default parameters :
DataFrameWriter.csv(
path,
mode=None,
compression=None,
sep=None,
quote=None,
header=None,
nullValue=None,
dateFormat=None,
encoding=None,
emptyValue=None,
lineSep=None
)
By using Delta tables, you can efficiently store and manage large datasets in Apache Spark, and take advantage of the scalability and performance of the Delta engine.
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