Column metadata for a Parquet file is stored at the end of the file, which allows for fast, single-pass writing. Therefore, without reading/parsing the contents of the file(s), Spark can simply rely on metadata to determine column names, compression/encoding, data types, and even some basic statistical characteristics. Unlike CSV and JSON, parquet files are binary files that contain metadata about their contents. Developers of the format claim that this storage format is ideal for solving Big Data problems. Since the data are stored in columns, they can be highly compressed (compression algorithms work better with data with low entropy of information, which is usually contained in columns) and can be separated. ➖ It is less compact as compared to over binary formats. ➖ JSON consumes more memory due to repeatable column names ➕ JSON is a widely used file format for NoSQL databases such as MongoDB, Couchbase and Azure Cosmos DB ➕ JSON supports lists of objects, helping to avoid chaotic list conversion to a relational data model ➕ Most languages provide simplified JSON serialization libraries or built-in support for JSON serialization/deserialization ➕ JSON supports hierarchical structures, simplifying the storage of related data in a single document and presenting complex relationships While the data contained in JSON documents can ultimately be stored in more performance-optimized formats such as Parquet or Avro, they serve as raw data, which is very important for data processing (if necessary). Many streaming packages and modules support JSON serialization and deserialization. With this huge support, JSON is used to represent data structures, exchange formats for hot data, and cold data warehouses. Since much data is already transmitted in JSON format, most web languages initially support JSON. They are therefore more commonly used in network communication, especially with the rise of REST-based web services. Both formats are user-readable, but JSON documents are typically much smaller than XML. JSON is often compared to XML because it can store data in a hierarchical format. JSON (JavaScript object notation) data are presented as key-value pairs in a partially structured format. Spark and MR) initially support serialization and deserialization of CSV files and offer ways to add a schema while reading. Similarly, most batch and streaming frameworks (e.g. ➖ Problems with CSV import (for example, no difference between NULL and quotes) ĭespite limitations and problems, CSV files are a popular choice for data exchange as they are supported by a wide range of business, consumer, and scientific applications. ➖ There is no standard way to present binary data No difference between text and numeric columns Complex data structures have to be processed separately from the format In CSV, the column headers are written only once For XML, you start a tag and end a tag for each column in each row. ➕ CSV can be processed by almost all existing applications ➕ CSV is human-readable and easy to edit manually One of the other properties of CSV files is that they are only splittable when it is a raw, uncompressed file or when splittable compression format is used such as bzip2 or lzo (note: lzo needs to be indexed to be splittable).
In addition, the CSV format is not fully standardized, and files may use separators other than commas, such as tabs or spaces. Foreign keys are stored in columns of one or more files, but connections between these files are not expressed by the format itself. Data connections are usually established using multiple CSV files. CSV files may not initially contain hierarchical or relational data. Essentially, CSV contains a header row that contains column names for the data, otherwise, files are considered partially structured. CSV is a row-based file format, which means that each row of the file is a row in the table. CSVĬSV files (comma-separated values) are usually used to exchange tabular data between systems using plain text. In this post, we will look at the properties of these 4 formats - CSV, JSON, Parquet, and Avro using Apache Spark. Common formats used mainly for big data analysis are Apache Parquet and Apache Avro. Apache Spark supports many different data formats, such as the ubiquitous CSV format and the friendly web format JSON.
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