Determining Efficient Data Queries Structure for Sporting Events Management

The State Changers discussed various implementation strategies for their product, including how to filter data and display relevant results. They specifically talked about dynamically showing sports competitions based on the activation of certain sport events, with a reference to a system used by another company ("Underdog") as a model.

A challenge arose from their desire to fetch and filter multiple data types in the same request. They discussed using add-ons in Xano, but found it limiting for their particular use case, leading them to consider alternatives. Multiple queries were suggested as a solution, although there were some concerns about possible performance penalties. A key point was the need to understand the whole data landscape before implementing solutions. The State Changers suggested reasoning about inputs and outputs with a series of queries, before determining performance factors and necessary optimizations. They also discussed using loops for data manipulation. Another important part of the meeting was addressing debugging challenges and the potential need for two separate API calls to fetch all the data and get specific competition IDs. The conversation ended with a decision to perhaps follow the approach used by "Underdog". Keywords mentioned in the meeting included Xano and API. No references were made to the following: WeWeb, FlutterFlow, Zapier, Make, Integromat, Outseta, Retool, Bubble, Adalo, AppGyver, AppSheet, Comnoco, Fastgen, Firebase, Google, OAuth, Stripe, Twilio, Airtable, DraftBit, Javascript, Typescript, React, Vue.js, JSX, HTML, CSS, lambda, serverless, State Change, ScriptTag, OpenAI, and AI21.

(Source: Office Hours 12/26 )

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