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There are several methods for compressing two files in Databricks, depending on the type of data and the desired outcome. Here are a few examples:

1. Using gzip: To compress two files using gzip, you can use the following command in a Databricks notebook:

import gzip
with open('/dbfs/path/to/file1.csv', 'rb') as f_in:
    with gzip.open('/dbfs/path/to/file1.csv.gz', 'wb') as f_out:
        f_out.writelines(f_in)

with open('/dbfs/path/to/file2.csv', 'rb') as f_in:
    with gzip.open('/dbfs/path/to/file2.csv.gz', 'wb') as f_out:
        f_out.writelines(f_in)

This will read in the contents of each file, compress them using gzip, and write the compressed files to disk with a .gz extension.

1. Using zip: If you want to combine two files into a single compressed archive, you can use the built-in zipfile library in Python:

import zipfile
with zipfile.ZipFile('/dbfs/path/to/archive.zip', 'w') as myzip:
    myzip.write('/dbfs/path/to/file1.csv')
    myzip.write('/dbfs/path/to/file2.csv')

This will create a new zip archive file at the specified path that contains both input files.

1. Using Spark: If you're working with large datasets and want to take advantage of Databricks' distributed computing capabilities, you can use Spark to compress data. For example, you could read in two Parquet files, merge them into a single DataFrame, and write the result back out to a single Parquet file with compression enabled:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("myApp").getOrCreate()

df1 = spark.read.parquet('/path/to/file1.parquet')
df2 = spark.read.parquet('/path/to/file2.parquet')

merged = df1.union(df2)

merged.write.parquet('/path/to/merged.parquet', compression='gzip')

This will create a new Parquet file that contains the combined data from both input files, compressed using gzip.