source: kdnuggets: top 7 python libraries for large-scale data processing
level: technical
python has many libraries for working with data at scale. when datasets grow to gigabytes and beyond, standard tools like pandas reach their limits. processing billions of rows, running distributed machine learning pipelines, or streaming real-time events requires libraries built for these tasks. this article covers libraries for datasets that exceed single-machine memory, distributed computation across cores and clusters, real-time and streaming data workloads, integration with cloud storage and data warehouses, and production-ready data pipelines.
pyspark is the python api for apache spark, used for distributed etl and cluster-scale pipelines. it runs batch and streaming computations across clusters with a dataframe api and integrates with hdfs, s3, and delta lake. dask scales pandas and numpy workflows to datasets larger than memory by breaking data into chunks and using lazy evaluation. it mirrors pandas and numpy apis, making it easy to scale existing code. polars is a dataframe library written in rust that uses the apache arrow format. it outperforms pandas on benchmarks and supports lazy query optimization for datasets that do not fit in memory.
ray is a distributed computing framework for scaling python workloads across clusters, with ray data for data ingestion and ray train for distributed model training. vaex handles billion-row datasets on a single machine using memory-mapped files and lazy evaluation. apache kafka is a distributed event streaming platform for high-throughput real-time data, with python clients like kafka-python and confluent-kafka. duckdb is an in-process analytical database that runs sql queries on local files without a server, integrating tightly with pandas and arrow. these libraries cover a range of needs from local analysis to distributed pipelines.
why it matters: choosing the right library helps data scientists and engineers process large datasets efficiently without hitting memory or performance limits.
source: kdnuggets: top 7 python libraries for large-scale data processing