Name | Size | License | Age | Last Published |
---|---|---|---|---|
moment | 681.9 kB | MIT | 12 Years | 6 Jul 2022 |
validator | 176.41 kB | MIT | 13 Years | 4 Aug 2023 |
fast-xml-parser | 29.23 kB | MIT | 7 Years | 30 Jul 2023 |
express-validator | 33.42 kB | MIT | 12 Years | 16 Apr 2023 |
lint-staged | 34.32 kB | MIT | 8 Years | 21 Aug 2023 |
aproba | 3.6 kB | ISC | 8 Years | 22 May 2018 |
@sinclair/typebox | 73.61 kB | MIT | 3 Years | 14 Sep 2023 |
yn | 2.53 kB | MIT | 9 Years | 14 Aug 2021 |
sanitize-filename | 6.3 kB | WTFPL OR ISC | 10 Years | 26 Aug 2019 |
jose | 69.05 kB | MIT | 9 Years | 4 Sep 2023 |
async-validator | 65.63 kB | MIT | 8 Years | 17 Jun 2022 |
validate.js | 22.56 kB | MIT | 10 Years | 15 Jun 2019 |
ip-regex | 2.7 kB | MIT | 9 Years | 1 Jan 2022 |
@sindresorhus/is | 17.83 kB | MIT | 6 Years | 15 Aug 2023 |
ts-interface-checker | 20.27 kB | Apache-2.0 | 6 Years | 11 Oct 2021 |
Data validation libraries are incredibly useful across a multitude of different contexts. It is imperative when dealing with data inputs and operations that the data is valid, secure, and follows the intended structure. Therefore, in JavaScript and Node.js applications, in particular, data validation is important to ensure reliability and security.
One instance when validation software considerably excels is when dealing with user input data. For example, in web applications, form data submitted by users can sometimes be incorrect, incomplete, or intentionally malicious. Here, using data validation dependencies ensures this data is safe and adheres to requirements before it processes.
In addition, these libraries can also address the normalization and sanitization of data received from other APIs or data sources. Using a data validation library can help to verify that the data received is as expected, and in case it's not, it could help outline the inconsistencies.
Overall, a well-equipped data validation library is a key component in developing and maintaining secure and reliable software applications.
Data Validation Software play a vital role in handling and ensuring the quality of datasets. They usually have the following functionalities:
All these features enhance the integrity, accuracy, and trustworthiness of the data.
When implementing data validation with npm modules, there are a few pitfalls you need to be aware of:
Remember that a meta-principle in programming is that all data are guilty until proven innocent. Never make assumptions about incoming data; instead, implement solid validation.