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Why is XML not commonly used for real-time data processing and analysis, and what are the alternative formats that are used instead?

XML is not commonly used for real-time data processing and analysis because it is not well-suited for high-performance, low-latency applications. XML parsing and processing can be slow, especially for large or complex XML documents, and its hierarchical structure can make it difficult to efficiently search and analyze large datasets in real-time.

As a result, alternative formats are typically used for real-time data processing and analysis, including:

1. Protocol Buffers (protobuf): A compact binary format that is designed to be efficient and fast, and is often used for high-performance, low-latency applications.

2. Apache Avro: A data serialization system that is designed to be compact and fast, and is well-suited for big data and stream processing applications.

3. Apache Parquet: A columnar storage format that is designed to be efficient and fast, and is often used for big data and analytics applications.

4. Apache Thrift: A software framework for scalable cross-language services development, which can be used for real-time data processing and analysis.

These alternative formats are typically more efficient and easier to work with than XML for real-time data processing and analysis, as they are optimized for performance and are often easier to process in parallel. Additionally, many of these formats have been designed with big data and stream processing in mind, and are well-suited for working with large datasets in real-time.