Apache Spark has emerged as one of the key big data technologies in recent years. As an open-source distributed general-purpose cluster computing framework, Spark provides an integrated platform for data engineering, machine learning, and real-time analytical workloads. Some of its major capabilities include:
In-memory caching and optimized query execution that makes Spark faster than preceding technologies like Hadoop MapReduce.
A unified engine that supports SQL, batch processing, streaming analytics, machine learning, and graph processing - eliminating the need to integrate separate tools.
An intuitive and expressive programming model that enables more productive data engineering and data science.
Native distributed machine learning library MLlib for easily building scalable machine learning models.
Highly versatile platform that can connect to diverse data sources and targets.
With these capabilities, Spark is well-suited for large-scale data processing at companies like Netflix, which need to derive value from huge volumes of data in real-time. By leveraging Spark, Netflix can build transformational data-driven applications for video streaming and recommendations.
