It can be the semicolon, space, or other characters, but the comma is most commonly used. The CSV doesn't rely on commas as separators between pieces of information, so this separator is called a delimiter. The CSV file separates the information using the commas that is a way to exchange the structured information like the contents of the spreadsheet, that is, the programs that can't necessarily talk to one another directly. The CSV file is also a simple text file that can be opened in a wide variety of programs, including any program that works with the plain text like the Notepad app or Notepad++ app so, a CSV file is unique in the way of its content is always organized. The CSV (comma-separated values) file is the simple text file in which the commas separate information.
The compact, binary serialization format provides fast while transferring the data.
Apache Avro supports complex data structures like Arrays, maps, Arrays of the map, and array elements. Also, When Avro data is stored in a file, its schema is stored with it so that files may be processed later by any program. Apache Avro is primarily used in Apache Spark, especially for Kafka-based data pipelines. Apache Avro is defined as the open-source, row-based data serialization and the data exchange framework for Hadoop and big data projects, which was initially developed by the databricks as the open-source library which supports the reading and writing data in the Avro file format. Spark SQL provides "("path")" to read the single line and the multiline(i.e., multiple lines) JSON file into Spark DataFrame and the "("path")" for saving and writing to the JSON file. The JSON file format is primarily used for transmitting data between a web application and a server. The JSON file is defined as a file that stores the simple data structures and objects in the JavaScript Object Notation(JSON) format, a standard data interchange format. Recipe Objective - How to convert the JSON file to the Avro, CSV, and Parquet files in Apache Spark?
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