CSV is a simple text-based format used to store tabular data where each line represents a row, and columns are separated by commas. It is widely supported by spreadsheet software and databases, making it a popular choice for data import, export, and analysis.

Parquet is a columnar storage file format optimized for large-scale data processing and analytics. It provides efficient data compression and encoding schemes, making it ideal for use with big data tools like Apache Spark and Hadoop. Parquet supports complex nested data structures and schema evolution.

XML is a flexible markup language that allows users to define custom tags for structuring data hierarchically. It is widely used in web services, configuration files, and data exchange systems. XML supports complex structures, metadata, and is both human-readable and machine-readable.

Avro is a compact, fast, binary data serialization system used primarily with Apache Hadoop. It uses JSON for defining data schemas and encodes data in a binary format. Avro supports schema evolution, making it highly suitable for big data applications and data pipelines.

CSV is a simple text-based format used to store tabular data where each line represents a row, and columns are separated by commas. It is widely supported by spreadsheet software and databases, making it a popular choice for data import, export, and analysis.

Parquet is a columnar storage file format optimized for large-scale data processing and analytics. It provides efficient data compression and encoding schemes, making it ideal for use with big data tools like Apache Spark and Hadoop. Parquet supports complex nested data structures and schema evolution.

XML is a flexible markup language that allows users to define custom tags for structuring data hierarchically. It is widely used in web services, configuration files, and data exchange systems. XML supports complex structures, metadata, and is both human-readable and machine-readable.

Avro is a compact, fast, binary data serialization system used primarily with Apache Hadoop. It uses JSON for defining data schemas and encodes data in a binary format. Avro supports schema evolution, making it highly suitable for big data applications and data pipelines.

CSV is a simple text-based format used to store tabular data where each line represents a row, and columns are separated by commas. It is widely supported by spreadsheet software and databases, making it a popular choice for data import, export, and analysis.

Parquet is a columnar storage file format optimized for large-scale data processing and analytics. It provides efficient data compression and encoding schemes, making it ideal for use with big data tools like Apache Spark and Hadoop. Parquet supports complex nested data structures and schema evolution.

XML is a flexible markup language that allows users to define custom tags for structuring data hierarchically. It is widely used in web services, configuration files, and data exchange systems. XML supports complex structures, metadata, and is both human-readable and machine-readable.

Avro is a compact, fast, binary data serialization system used primarily with Apache Hadoop. It uses JSON for defining data schemas and encodes data in a binary format. Avro supports schema evolution, making it highly suitable for big data applications and data pipelines.