How can you validate data in software applications with real-time processing?

Data is one of the most important pieces of any software, and consequently, its validation is necessary for any organization to achieve business and technology goals. In summary, data quality is directly proportional to software usability and reliability.

If you are building a REST API, break down the validation into two steps: - Syntax validation - Semantic validation Syntax validation validates the incoming data structure. You're better off using a library to do this kind of validation. If the above validation passes, the incoming request data should go through a semantic validation fitler—which typically lives in the business logic. Here's where validation for meaning happens. Like valid ID checks and other conditional business logic. If the data passes these two stages, you're good to process it.

Validating data during the software development life cycle is crucial to ensure the accuracy, reliability, and integrity of the information processed by a software system. I can outline some best practices for validating data throughout the different stages of the SDLC. 1. Clearly define data requirements in collaboration with stakeholders. In this phase, the source of truth should be determined and constraints should be identified. 2. Document requirements and rules identified. Make use of diagrams whenever necessary. 3. Apply input validation on the client side to provide immediate feedback to users. Implement server-side validation to ensure data integrity and security. Also, implement validation checks at the database level.

In addition to the one already covered, some of the real world issues faced during data validation are: 1. Dealing with data that arrives out of order is a common challenge. Whether it's due to network delays or any other external factors, designing a validation method that can properly handle and authenticate this out-of-sequence data without sacrificing accuracy is a challenge. 2. It is important to implement data validation mechanisms that handle errors gracefully without causing the entire system to crash. This requires designing strong error-handling strategies and mechanisms for recovering from validation failures.

In my experience determining rules and requirements during the requirements-gathering phase and communicating them to all stakeholders clearly is the most challenging aspect of data validation. Many of the faults encountered in the production environment, are due to communication issues related to how data should be validated and how it should flow through different modules.

Separate the validation logic into syntax and semantic layers. Rely on a library to do syntax validation. The semantic validation can be inside of the business logic, but it should happen just before the data is processed. If you're building a REST API, make sure to return a 400 series error code if the data validation fails. Learn more about the commonly used HTTP response codes before writing your validation layers. It's a good idea to log all the errors in logging stream (and also in the database if required). Data like this can be helpful for later troubleshooting based on customer issue reports if needed.

To validate JSON schema, one of the best libraries available in Python is the Voluptuous library. It is stable, featureful and we've used it in production systems for over a dozen client applications. If you are using pytest for testing, the pytest-voluptuous library is a handy plugin that you should be aware of.

For Asp.net: 1. You can use attributes like [Required], [StringLength], and [RegularExpression] to annotate your model 2. FluentValidation is a third-party validation library that allows you to define validation rules 3. Serilog is a logging library for .NET that provides a flexible and extensible logging framework. For Azure Cloud Platform: 1. Azure API Management allows you to define policies, including validation policies, for your APIs. You can also use monitoring tools like Splunk, and Data Dog for reports, monitors, and dashboards to keep track of data flow and exceptions.

Here's the order in which I recommend REST API developers to write software: - Define the API schema and share it via a tool like Postman - Write the JSON validators (syntax validation layer) first - Write the semantic validation logic - Finally, write the business logic You're gonna build production level APIs easily and much quicker if you follow this order.

Periodically check logs in production for exceptions. Some faults can go undetected and can hurt business outcomes and revenue.