Wie skaliert man Python-Skripte für die Verarbeitung umfangreicher Daten?

It's important to start small and with the basics. You can get quite far by only leveraging the low-hanging optimizations. One of the easiest is going to be code optimization. Just ensure that you don't have duplications, unnecessary loopings, or multiple calls to the database or APIs for the same data. Furthermore, you can see if you're leveraging the Python constructs such as generators, and proper data structures.

Once you have done code optimization, the next stage is to start thinking about leveraging the parallel processing. Python itself may not have that ability however there are quite a few good libraries that can help you to achieve multiprocessing to spread tasks across multiple CPU cores and have the execution in parallel. You can use processes or consider using IronPython for proper multi-threading capabilities in Python.

A distributed system is the key to large data processing. There are a lot of frameworks there and new ones are coming out but my personal favorite is Apache Flink. It allows you to distribute the data on different systems and have them processed in parallel in a distributed manner on a cluster of computers. PyFlink is Flink's python library that can be used to develop applications in Flink using Python.

If you need to process data in real time, you should use a stream processing approach. Batch is where we need the whole data to process, however, with Stram processing you can process the data in real-time as it is coming. We can use Apache Flink with one of the messaging systems such as Apache Kafka, and RabbitMQ to ingest the data in realtime and Flink can process it. Streaming infrastructure can complex and should be only used when it's needed such as for realtime analytics.

As long as each chunk fits in memory, you can work with datasets that are much larger than memory. Chunking works well when the operation you're performing requires zero or minimal coordination between chunks. For more complicated workflows, you're better off using other libraries.

There are several tools and frameworks in Python that can help you process large amounts of data efficiently. Based on my experience, Polars and DuckDB are two promising tools that enable you to scale easily and provide a good developer experience. These tools come in handy when you need to process data on a single machine. However, if the data is too big to fit in a single machine, you can consider using Apache Spark, Apache Beam, or a data warehouse like BigQuery. An exciting thing about BigQuery is that it now supports data frames with Python, allowing you to use familiar Python abstractions while running everything inside BigQuery.