What Are Data Formats? Common Types Explained

Countless data formats are out there in the digital world, but a handful have become standards for specific use cases, from simple spreadsheets to massive-scale analytics.

These may include, but are not limited to:
 

1. CSV format

CSV (comma-separated values) is a simple, text-based tabular data format dating back to the 1960s: Each line represents a row of data, and fields in a row are separated by commas. It is the most commonly used format for exporting spreadsheets or databases. The CSV format’s strengths are in the simplicity and interoperability of this data format, which leads to small and easily compressed files, and ease of reading even by humans. But that also means that CSV files are not suitable for complex or large data sets due to their simplicity and lack of metadata.  
 

2. JSON (JavaScript Object Notation)

JSON is also a lightweight text-based data format for storing and sharing data, and humans and computers can read it easily. It was created in the early 2000s as a lighter alternative to XML for data interchange, particularly across web applications. The JSON data format is structured with curly braces { } holding an object (a set of key-value pairs); with square brackets [ ] holding an array (an ordered list of items); and with other elements such as keys (in text quotes) and various values. JSON’s strengths are that it’s easy to read and write, it’s language-independent (useful beyond just JavaScript), is widely used in APIs (how apps talk to each other) and is good for storing structured data. JSON supports more complex data structures than CSV, including nested objects and arrays.
 

3. XML file format

XML (eXtensible Markup Language) was created by the World Wide Web consortium (W3C) in the mid-1990s as an alternative to HTML, which is great for displaying data, but the growing demands of large-scale electronic publishing needed an alternative format to better store and transport data. XML is also a text-based data format for structuring, storing and transmitting data to enable sharing information between systems such as websites, apps and databases. It’s also both human and machine readable. XML files contain data in the form of text, along with tags, which define what the text or data is, such as “first name” or “date,” that software can  read and organize. XML’s strength is that it can be shared across systems and platforms since it stores data in a standardized format that different software applications can read. The hierarchical system of tags also allows ease of searching across the web. 
 

JSON vs. XML

One of the downsides of using XML is that its verbosity leads to larger file sizes and slower processing due to its need for both an opening and closing tag. This results in significant overhead, since sometimes the tags can take up more room than the actual data they’re categorizing for storing. JSON is a modern alternative to XML for this reason: Its compact format requires less processing power. XML has become less popular now, with CSV and JSON more widely supported. 
 

4. Parquet file format

Today’s era of big data and its performance and scalability needs prompted a group of Twitter and Cloudera engineers to create Parquet, a columnar data file format useful for large data sets and real-time big data processing. It was launched in 2013 as an Apache Hadoop project, for an alternative to the inefficiencies of row-based data formats like CSV, and now Parquet is popular for high-performance data applications. Despite their capability to handle large data volumes and processing, Parquet data files are efficient and their advanced compression technology minimizes storage needs. The columnar format is more efficient than rows since it minimizes I/O operations. Another way to look at it is that Parquet’s strengths align with data-reading needs. The Parquet file format is ideal for big data warehousing and analytics, querying and data retrieval.

However, Parquet is inefficient for updates and writing since buffering data and writing to columns is more resource-intensive than writing to rows. As a binary data file format (not text-based), humans can’t open and read Parquet.
 

5. Avro format

Avro is an Apache project known as a data serialization system. Its binary, row-based format is compact and efficient, used in big data environments for fast data storage and retrieval. Avro is also language-neutral, meaning that it can handle data exchange efficiently between different systems and programming languages. One of its main strengths is its robust support for schema evolution. Avro stores the schema as part of the data file, and handles schema changes gracefully. If fields are added or removed, old and new readers can still process the data, which is helpful for long-running data pipelines.

Since Avro is row-based, it’s inefficient for analytical queries that might involve only a few columns, because a query engine would still need to read the entire row (thereby slower than column-based formats like Parquet).  
 

6. ORC format

ORC (optimized row columnar) is a columnar storage file format also designed for big data. ORC is optimized for read-heavy analytical workloads in the Hadoop ecosystem, especially with tools like Apache Hive and Spark. Its key features include efficient compression to reduce storage requirements, data skipping to enhance read efficiency, and in-file indexes, helping minimize the amount of data read from disk to satisfy a query and speeding up performance for large-scale data analysis.

ORC supports efficient writes and ACID transactions, which makes it a versatile format for data warehousing, but its core design is to optimize for data retrieval. While both Parquet and ORC are columnar formats suitable for read-heavy workloads, ORC supports more write-heavy applications, for example environments that need both heavy reads and frequent data updates. 

ORC doesn’t have as wide support as some other data file formats, which might be a limiting factor in certain ecosystems.
 

7. TXT format

TXT represents the most basic and universal file type, with no formatting like different font styles, structure, metadata or embedded images. That creates small, compact files for storage and transfer. It also means that not only can humans read TXT files — almost any operating system,  application, text editor or programming environment can read them, too. This high compatibility is the TXT file’s strength, but its simplicity also creates limitations. Without structure or rich formatting, TXT files are inefficient for storing complex data sets. TXT files are also considered row-based like CSV files, and reading whole rows to get a single value is slower and less efficient than reading column-based formats like Parquet and ORC.

Data formats have been created and evolved through the digital decades to accommodate changing needs, from compact simplicity to efficiency while accommodating big data set analytics. Data professionals must know the strengths and weaknesses of each data file format to choose the appropriate one for whatever data management task or environment they are working on.

The evolution of data management points toward the rise of flexible, integrated cloud platforms. These unified systems are designed to seamlessly ingest, store and process a vast range of data formats — from traditional text and row-based files like CSV to modern columnar formats like ORC and Parquet — all within a single, cohesive environment. This shift enables organizations to break down data silos and unlock the full potential of their information, ensuring that any workload, whether for real-time analytics, machine learning or business intelligence, can be efficiently processed. The future isn't about choosing one format over another but about having the agility to use any or all in harmony.