New to Py Spark, parquet and delta ecosystem and little confused on seeing multiple ways to play with delta files.

Can someone help me understand on which one is correct or preferred for updating a delta file ? Directly operating on underlying delta file OR first create a table and then run SQL query on it.

My eventual goal is to run a pyspark script on a 10-million record file, so do share any performance tips to keep in mind.

Method 1: SQL – Read table into dataframe, create a temp view/table and run a query using sqlContext

deltadf = spark.read.format("delta").load(delta_format_tablename)
deltadf.createOrReplaceTempView("tempEmployee")
sqlContext.sql("DELETE FROM tempEmployee where id > 1200")
 
#Would this update the underlying delta file

Method 2: Directly on Table – Create delta table object and do operation directly on it.

deltaTable = DeltaTable.forPath(spark, delta_format_tablename)
deltaTable.delete("id > 1200")

Method 3: Dataframes ( no tables ) – Read table into dataframe, and iterate through it

deltadf = spark.read.format("delta").load(delta_format_tablename)
for i in range(deltadf.count()): 
    # perform action like update / delete etc 

#now sure how to update the underlying delta file

Also I do not get this statement, it is saving a df to a file while simultaneously creating a table. Would updating the table (using some sql , automatically update the file ? )

## specify a path option to save as an external table
df.write.format("delta").option("path", "/mydata").saveAsTable("MyExternalTable")

Thanks.