Which framework, designed for large-scale data processing and analytics, was initially released in 2014?

The choice of Apache Spark is accurate as it was designed specifically to handle large-scale data processing and analytics and was released in 2014. Apache Spark provides a fast, in-memory data processing engine that excels in providing high performance for both batch and stream processing tasks. Its architecture allows for efficient handling of big data workloads and supports various programming languages, making it versatile for users across different domains.

In addition to its speed and ease of use, one of the highlights of Spark is its ability to perform complex data processing tasks such as machine learning, graph processing, and real-time analytics within the same framework. This all-in-one approach simplifies the workflow for data engineers and data scientists, as they can execute various data operations without needing to switch between different tools.

Though Apache Flink, Apache Storm, and Apache Hadoop are all prominent frameworks in the realm of big data processing, they either do not have the same level of integration and ease for various data operations or were released earlier than 2014. For instance, Apache Hadoop, while foundational for big data architecture, was initially released in 2006 and does not have the same emphasis on in-memory processing as Spark. Understanding the capabilities and release timelines of these frameworks highlights why Apache Spark stands out for the specified