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You can check the scrolling direction like so

Acording to this answer: https://stackoverflow.com/a/51236918/16651073 tomcat falls back to default logging if it can resolve the location

Can you try to save the properties without the spaces.

Like this: logging.file.name=application.logs

Following should work.

It looks like that you're using incompatible version of the Delta lake library. 0.7.0 was for Spark 3.0, but you're using another version - either lower, or higher. Consult Delta releases page to find mapping between Delta version & required Spark versions.

If you're using Spark 3.1 or 3.2, consider using delta-spark Python package that will install all necessary dependencies, so you just import DeltaTable class.

Update: Yes, this happens because of the conflicting versions - you need to remove delta-spark and pyspark Python package, and install pyspark==3.0.2 explicitly.

P.S. Also, look onto pytest-spark package that can simplify specification of configuration for all tests. You can find examples of it + Delta here.

This is possible by renaming the single parquet file in the dataset so that it always has the same file name, that way the export task will overwrite the previous file in the external system.

This can be done using raw file system access. The write_single_named_parquet_file function below validates its inputs, creates a file with a given name in the output dataset, then copies the file in the input dataset to it. The result is a schemaless output dataset that contains a single named parquet file.

Notes

• The build will fail if the input contains more than one parquet file, as pointed out in the question, calling .coalesce(1) (or .repartition(1)) is necessary in the upstream transform
• If you require transaction history in your external store, or your dataset is much larger than 512 MB this method is not appropriate, as only the latest version is kept, and you likely want multiple parquet files for use in your downstream system. The createTransactionFolders (put each new export in a different folder) and flagFile (create a flag file once all files have been written) options can be useful in this case.
• The transform does not require any spark executors, so it is possible to use @configure() to give it a driver only profile. Giving the driver additional memory should fix out of memory errors when working with larger datasets.
• shutil.copyfileobj is used because the 'files' that are opened are actually just file objects.

Full code snippet

example_transform.py

Yes, that's how Delta lake works - when you're doing modification of the data, it won't write only delta, but takes the original file that is affected by change, make changes, and write it back. But take into account that not all data is duplicated - only that were in the file where affected rows are. For example, you have 3 data files, and you're making changes to some rows that are in the 2nd file. In this case, Delta will create a new file with number 4 that contains necessary changes + the rest of data from file 2, so you will have following versions:

• Version 0: files 1, 2 & 3
• Version 1: files, 1, 3 & 4

As can be seen in the comments of the post, my problem is fixed, so I thought I'd collect a closing answer here to not leave the post pending.

The actual solution: As explained here nicely by @UninformedUser, the issue was that I had conflicting maven package versions in my dependencies. Bringing everything in sync with each other solved the issue.

Incidental solution: As I wrote in the comments above, specifically defining 3.3.0 for the maven-assembly-plugin happened to solve the issue. But this was only chance, as explained here by @Ignazio, just because the order of "assembling" things changed, overwriting the conflicting package.

Huge thanks to both for the help.

You are correct. Option 2 is just option 1 under the hood.

What is the fastest way for me to read a Parquet file into Pandas?

Both option 1 and option 2 are probably good enough. However, if you are trying to shave off every bit you may need to go one layer deeper, depending on your pyarrow version. It turns out that Option 1 is actually also just a proxy, in this case to the datasets API:

The raw file s3://nyc-tlc/trip data/yellow_tripdata_2010-02.csv contains an error (one too many commas). This is the offending line (middle) and its neighbours: