什么是 Apache Spark？

This article lists and reviews seven top Python libraries for large-scale data processing, including PySpark, Dask, Polars, Ray, Vaex, Vaex-Java, and Vaex-Python.

Leverages Apache Spark Real-Time Mode and transformWithState to deliver a unified, sub-second real-time sessionization architecture, replacing Flink or in-house solutions to power personalization, recommendation engines, and dynamic content scheduling for millions of players.

Google Cloud announces gcs-analytics-core, an open-source Java library designed to optimize Apache Iceberg and Spark workloads on Google Cloud Storage, achieving significant performance improvements through parallel I/O and smart Parquet prefetching.

Lightning Engine 提升 Apache Spark 性能达 4.9 倍，通过原生执行和优化连接器实现。

Google Cloud has introduced several enhancements to Managed Spark clusters, including Lightning Engine, Flexible VMs, and Gemini-powered extensions, significantly improving performance and flexibility.

Google Cloud has announced the general availability of the serverless Managed Service for Apache Spark runtime version 3.0, prioritizing speed, simplicity, and reliability. This update reduces startup times by 75%, improves GPU obtainability with Dynamic Workload Scheduler Flex Start Mode, and supports Apache Spark 4.x innovations.

Apache Kafka is transitioning towards a cloud-native architecture, changing its economic model through storage disaggregation, reducing operational costs, and improving flexibility.

This article introduces the basic concepts and core mechanisms of PySpark to help beginners understand how to process large-scale data with Python.