Create a Search Engine and Other Startup Ideas Using Data-Ferret

Data-ferret is a tiny, yet powerful util library to scan or transform deeply nested and complex object-like data with ease. It is available as an open-source project under an MIT license.

It can search and transform complex, data whose interface or shape cannot be guaranteed (schemaless), in other words, messy data.

data-ferret is designed to be extensible. It supports custom class instances or iterables, beyond the native JavaScript data types.

Additionally, it provides first-class support for handling objects with circular references.

I would like to give away to my readers my top 5 picks for viable SaaS products that could benefit from using data-ferret to build a business.

Startup Ideas

1. A Search Engine

One of the most potential applications for data-ferret is building the quintessential search engine. You can use data-ferret’s locateText() function or create your custom traverse() function for specific capabilities.

This can be applied to various industries as a SaaS product. For example;

  1. A one-stop shop to find jobs and real estate opportunities by using data from various sources and third-parties listing providers. locateText() or your custom traverse() will not need data to be normalized to work with it. Users can be presented with all properties available in the dataset by using getUniqueKeys().

  2. The same concept may apply to a social media platform that allows users to search for posts, photos, and other content based on keywords, hashtags, and other criteria, without needing to know the specific schema of the data.

  3. Same for goes news search engine where specific text can be matched or any tags or flags that may exist in the dataset or a research paper search engine that allows users to search for academic papers based on keywords, authors, and other criteria, without needing to know the specific schema of the data.

  4. A customer service chatbot that allows users to search for answers to common questions and problems, without needing to know the specific schema of the data.

  5. A legal search engine that allows users to search for case law and other legal documents based on keywords, judges, and other criteria, without needing to know the specific schema of the data.

  6. A medical search engine that allows users to search for medical information and research based on keywords, conditions, and other criteria, without needing to know the specific schema of the data.

2. Content Redaction/Moderation Service

Perhaps your next product idea has GDPR considerations or will have to deal with sensitive data like credit cards, or details that must be anonymized. Or in general, content moderation must be implemented.

You can leverage replaceText() to perform transformations on your dataset and generalize your approach without having to worry about interface/schemas changing.

3. Data Migration Service

A data migration tool for companies to seamlessly transfer data from one system to another, regardless of the data’s structure or format.

You could use getUniqueKeys() in conjunction with locateKey() to map out the schema of the original dataset and through a dashboard UI, a user can present a new schema as output. Key names may require renaming or deletion, for that renameKey() and removeKey() got you covered.

4. Data Visualization

Data-ferret ships a browser-ready version of the code, which means it is not just suitable for Node.Js backend services but also frontend.

A data analytics tool for businesses to easily process and visualize large amounts of data from various sources, without needing to know the specific schema of the data sounds like a plausible use case.

Data-ferret could be used on the backend to consolidate or prepare the initial data. On the front end, a UI can use the same APIs to perform quick search operations on the client side. Web workers could also be used to perform computationally heavy operations in a separate thread to ensure the main thread remains unencumbered to render the page and remain responsive.

5. Data Auditing

Sometimes the focus is on what data is there, not necessarily on how it is structured. For example, a data reconciliation tool for financial institutions to automatically match and reconcile transactions across multiple systems and data sources may use a custom traverse() function or locateKey(), which could let sniff out missing records, incorrect values, etc, with more ease, and then later apply whatever business rules make sense, whether it be generating a report or applying data correction.

That’s it. I hope this article has got your creative gears going and might prompt you to check out my project.

Good luck!