The questions you should ask to evaluate data platforms robustness

When designing new data platforms, failing to perform a complete assessment of the options available can have disastrous effects on a company's capability to properly operate, scale and make data secure, discoverable, usable and reusable.

The set of questions to ask during the evaluation can differ depending on the requirements, the existing or new tech available and the team knowledge. But applying a standard approach and framework can help comparing all the possible options.

The SOFT methodology provides a set of guidelines and questions, based on years of practice, to help consistently evaluate a data platform solution. The methodology analyses data solutions across various axis:

• Human, financial and technical Scalability helps understanding how to cope with variations of demand and different use cases.
• Observability addresses the crucial aspects of monitoring, data re-usage, and reproducibility of results.
• Time to develop, deliver and recover are good metrics to evaluate how Fast we can generate the desired outcomes.
• Security, vendor assessments and data correctness help define Trustworthy solutions.

For each of the four pillars, the SOFT methodology defines the questions you should ask yourself when performing the evaluation. Some example:

Scalability

• What options are there to scale this technology, vertically and horizontally?
• What is the current experience of the team?
• How easy is it to add/extract new data, or integrate with other technologies?
• What will scaling cost? Does it grow linearly with the data volume?

Observability

• What's happening now in my data platform?
• What data assets do I have across my company?
• How is my data transformed across the company?
• Can I replay part of the pipelines in the event of processing errors?

Fast

• How much delay is there between the source event and the related insight?
• How fast can we create new pipelines?
• How fast can I recover from a crash?

Trustworthy

• How can I secure my data? Can I mask it, and apply filters to rows/columns?
• Can I precisely locate my data at any stage of my flow? Both from a technical and also geographical point of view?
• Can I trust the data? Is it correct, are results repeatable, on time, and consistent?

If you want to read more, check the?Aiven?blog.