Huwise hacks & tips

You notice that your published dataset contains more records than your source file. For example, your file has 22,000 rows, but the dataset shows 24,000+. Clearing the cache and republishing does not fix the issue.Root causeThis is a known behaviour with the XLSX extractor on FTP sources.When a new file is deposited on the FTP under a different name than the previous one, the platform does not recognise it as a replacement; it treats it as an additional file. As a result, records from both the old and new files accumulate in the dataset instead of the old ones being replaced.The XLSX extractor does not detect deleted files in an FTP folder. Unlike CSV, it has no mechanism to automatically flush previous data when a new file with a different name is added.Solutions✅ Recommended — Keep the same filenameThe cleanest fix is to always use the same filename when replacing a file on the FTP. When the platform detects an update to a known file (same name), it correctly flushes the old data bef

Vous vous posez une question simple (et très utile) :“Quels sont les utilisateurs qui consomment le plus mon API, et sur quelle période ?”Que ce soit pour identifier vos power users, repérer un pic d’activité, ou préparer une action de communication ciblée, avoir un classement des utilisateurs les plus actifs est un excellent point de départ. La solution : sans export, sans requête complexeBonne nouvelle : votre portail enregistre déjà tout ce qu’il vous faut.Le dataset ods-api-monitoring trace chaque appel API effectué sur votre portail en temps réel. En quelques paramètres, vous pouvez extraire un top utilisateurs par volume d’appels sur la période de votre choix. ✅ La requête (Top 50 sur 12 mois)GET {{https://{votre-portail}}}/api/v2/monitoring/datasets/ods-api-monitoring/records ?select=user_id, count(*) as nb_api_calls &where=timestamp >= now(years=-1) &group_by=user_id &order_by=nb_api_calls desc &limit=50Cette requête retourne le top 50 des utilisateurs ayant

If you need to accurately calculate the gap between two key moments, the DateDiff function in the Expression processor is your best ally. 🔍 What is DateDiff?It’s a date and time function that counts the number of units (days, hours, minutes, etc.) between two datetime expressions. 🛠️ How do you implement it?In your Expression processor, the syntax usually follows this logic:DateDiff(unit, start_date, end_date)Unit: choose what you want to count ("day", "hour", "minute", etc.). Start_date & End_date: the two datetime fields you want to compare. 📖 Concrete exampleImagine you want to calculate the age of an alert in days:DateDiff("day", Alert_Created_Date, Current_Timestamp) 🌟 Why is it useful?Automation: no more manual calculations or complex scripts. Accuracy: ideal for monitoring your SLAs or account velocity. Reporting: lets you build powerful KPIs directly at the data source.And you — what’s your favourite function in the Expression processor for saving time? Share your tips