Detailed Introduction
Apache Spark is a unified analytics engine for large-scale data processing, offering multi-language APIs for Scala, Java, Python, and R. It provides a high-performance distributed computation framework with resilient data abstractions (RDDs, DataFrame/Dataset) and unifies batch processing, stream processing, and machine learning in a single platform, enabling consistent APIs for complex data pipelines in both single-node and cluster environments.
Main Features
Spark delivers a unified multi-language API (DataFrame/SQL), an optimized execution engine with in-memory computation and scheduling optimizations, Structured Streaming for low-latency stream processing, and MLlib for distributed machine learning algorithms. Its ecosystem integrates with Hadoop, Kafka, Delta Lake and many other storage and compute components.
Use Cases
Suitable for large-scale ETL, offline batch analytics, real-time stream processing, interactive querying, and large-scale ML training and inference. Typical uses include data engineering pipelines, reporting and dashboard backends, log analytics, feature engineering, recommendation systems, and model training workloads.
Technical Features
Spark uses a distributed DAG execution engine that supports lazy evaluation and task fusion optimizations, with scalable resource scheduling and fault tolerance. Its modular design (Spark SQL, Streaming, MLlib, GraphX) allows flexible composition, and it benefits from a large open-source community and long-term release maintenance.