Modern technology requires working with large amounts of data. Selecting the right data format can help optimize your project costs and system performance

Data is used for building algorithms, designing applications, programming machine learning capabilities, and training artificial intelligence. You need data storage formats that can be easily stored, transferred, retrieved, used, and modified to gain actionable insights.

If you want to export your excel sheet to a data file like MySQL, you must use a suitable data format. Some of the most commonly used data formats are CSV AVRO, PARQUET, XML, and JSON. They can be used in data science projects to build deep learning algorithms and machine learning capabilities. This article discusses the two most commonly used data formats for AI training; CSV and JSON.

CSV or JSON – Why Data Format Matters

Data can be generated internally or externally and stored in different data formats. Data formats are structured in different ways, which can affect their processing speed and space requirements. The type of data format you choose can impact the space requirement, data spitability, scalability, compression support, compatibility, cloud storage cost, and performance speed of your project.

You can consider several factors before determining your data format. You can save money by switching to CSV file format if you are handling large data sets from cloud storage. However, a JSON file may be a better option if you are running smaller data sets with complex hierarchy or relation sets. Using the right data format can save you money, time, and manpower.

There are many factors to consider while choosing the right data format. Ideally, you should test each data format on space utilization, level of structuring, complexity in data management, ingestion latency, random data lookup latency, data processing latency, basic statistics like min-max count, etc.

CSV File: Ideal for Big Data Analysis

The CSV file is a text-file format called Comma Separated Values and comes with the file extension .csv. The format stores data in simple, readable text separated by commas. Each line in a CSV file represents a row of data, while each column is represented by a delimiter like a comma or a semicolon. The varying elements of each record are recorded as columns separated by commas.

A CSV file can maximize space utilization and exchange data in tabular form between two AI systems. Structured CSV files may contain a header that classifies each element (column). CSV file format can also be separated into columns through delimiters like tabs and spaces.

CSV is widely used in developing business and consumer tech applications. Most data processing software can import, convert, and export CSV file data. You can easily serialize and deserialize CSV formatted spreadsheets using data processing software. CSV files are preferred for their simplicity and can be used by any data scientist to examine basic data structures.

It is also hard to read and prone to human errors. Even though CSV files have several limitations in terms of complex data structuring, they can handle big data, consume less space, and write/process data faster. CSV does not support complex data structures requiring relations and hierarchies between different data sets. CSV files are compact and more secure than JSON files. However, it may not be easily integrable with APIs, which makes it harder to scale.

JSON file: Ideal for Complex Data like Relational Models

JSON, also known as JavaScript Object Notation, is a serialization file format and easier to understand for humans. It was developed in 2001 from JavaScript. Data is represented as a semi-structured key-value pair and is relatively lightweight than AVO, PARQUET, or XML. A JSON file is widely compatible as most developers use it while designing configs and APIs.

JSON, being a subset of JavaScript, can be integrated with any Java-based environment, which is widely used for front- and back-end data processing. You can easily copy-paste a JSON onto your company's JavaScript to access new data or integrate the JSON file. A JSON is also easier to work with than XML since there are few opening or closing tags. JSON files offer relational and hierarchical data management. They are self-describing and don't take much effort for systems to recognize and work with JSON files. It is much easier to parse than XML and more easily readable than CSV files.

JSON offers varying levels of data complexity and management features. A typical JSON file supports the following data types.

• Strings– Texts like, "Profits", "Date", "
• Numbers– In any Format (decimal, percentage, fraction, etc.)
• Boolean Logic– True or False
• Null– no value
• Arrays– List of any type of data
• Object– Used for Key-Value Pairing between various data types

How to Convert CSV file to JSON

CSV and JSON are flexible storage formats since they are semi-structured data sets and you can easily retrieve the original data structure. You may need to convert a CSV file to a JSON file while processing and transferring data between two dissimilar systems.

You can easily convert any CSV file to JSON for easier integration and wider compatibility. You can use Converter App to convert any CSV to JSON file online without downloading any additional software. You can use the Converter App on any web browser through Windows, Android, or Mac OS. You can manually upload your JSON file from your computer or drag-drop it into the converter app box.

Another popular way of converting CSV to JSON is from the NPM software registry. Simply type in the command "npm install csv2json" on your OS command prompt. Next, type "csvtojson filename.csv> filename.json" to complete the conversion. You can convert any large data set and also introduce data-selection conditions.