Final Answer:
The Kinesis Connector Library is designed to run on (A) Apache Spark, facilitating scalable data processing, (B) Java for cross-platform compatibility and access to a rich ecosystem, and (C) Python, enhancing accessibility in data science and machine learning applications. The option (D) encompasses all these platforms, showcasing the library's versatility in adapting to diverse data processing environments.Thus option d is the correct option.
Step-by-step explanation:
The Kinesis Connector Library is designed to run on multiple platforms, making it versatile and adaptable to various data processing environments. It supports (A) Apache Spark, a powerful open-source distributed computing system commonly used for large-scale data processing.
Additionally, it is compatible with (B) Java, a widely used programming language that provides a robust and portable execution environment. Furthermore, the library can operate seamlessly with (C) Python, another popular programming language known for its simplicity and readability.
In more detail, the compatibility with Apache Spark ensures efficient and scalable processing of data streams, enabling real-time analytics and insights. The utilization of Java offers the advantage of cross-platform compatibility and access to a vast ecosystem of libraries and tools. The inclusion of Python support enhances the library's accessibility and integration capabilities, as Python is widely adopted in data science and machine learning applications.
In essence, the Kinesis Connector Library's ability to run on Apache Spark, Java, and Python (option D) reflects its versatility, enabling developers to choose the most suitable environment for their specific use case. This flexibility is crucial in the dynamic landscape of data processing, where diverse technologies are employed to meet varying business requirements.
Therefore option d is the correct option