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.