Export and Import IAM Configurations with Python and Kong Gateway

This article introduces a #Python tool for exporting and importing @InterSystems API Manager (IAM) configurations, which runs on Kong Gateway to manage API gateways in #InterSystems #IRIS. The script makes it easy to move and sync settings between environments, keeping everything neat and automated. 👉 https://lnkd.in/gnd6TnNm

This article introduces a #Python tool for exporting and importing @InterSystems API Manager (IAM) configurations, which runs on Kong Gateway to manage API gateways in #InterSystems #IRIS. The script makes it easy to move and sync settings between environments, keeping everything neat and automated. 👉 https://lnkd.in/e4sjfnke

This article introduces a #Python tool for exporting and importing @InterSystems API Manager (IAM) configurations, which runs on Kong Gateway to manage API gateways in #InterSystems #IRIS. The script makes it easy to move and sync settings between environments, keeping everything neat and automated. 👉 https://lnkd.in/e4sjfnke

This article introduces a #Python tool for exporting and importing @InterSystems API Manager (IAM) configurations, which runs on Kong Gateway to manage API gateways in #InterSystems #IRIS. The script makes it easy to move and sync settings between environments, keeping everything neat and automated. 👉 https://lnkd.in/gN62r_zh

This article introduces a #Python tool for exporting and importing @InterSystems API Manager (IAM) configurations, which runs on Kong Gateway to manage API gateways in #InterSystems #IRIS. The script makes it easy to move and sync settings between environments, keeping everything neat and automated. 👉 https://lnkd.in/eAcErup9

كل عام وأنتم بخير عيد سعيد للجميع As a gift for Eid, we have released our Python SDK. It gives you full programmatic control over your labeling projects, from creating tasks in bulk to exporting results. https://lnkd.in/e9RYcwtP

Python TIP : filter() vs List Comprehension After working with Python in production systems for years, one thing I’ve noticed is how often we need to filter data efficiently.... especially in backend services and data pipelines. A simple example: filter(lambda amount: amount > 800, transactions) What this does: • Iterates through each item • Applies the condition (amount > 800) • Returns only the matching values Example output: [900, 1300, 2200] My take after using this in real projects: • filter() is concise and works well in functional-style pipelines • It’s useful when chaining transformations (especially with map()) • That said, in many production codebases, I still prefer list comprehensions for readability Equivalent using list comprehension: [amount for amount in transactions if amount > 800] Why this matters: • Readability often beats cleverness in team environments • Consistency across the codebase is more important than personal preference • Choosing the right approach depends on context, not just syntax One quick reminder: filter() returns an iterator, so wrap it with list() if needed. After years of writing and reviewing code, I lean toward clarity first, but it’s always good to know both approaches. #Python #Programming #SoftwareDevelopment #Coding #Developer #PythonTips

What if you could call Python from C# in 4 lines — with Native AOT support, no project file, and compile-time security checks? That's DotNetPy, and v0.5.0 is now live on NuGet. Unlike existing options (pythonnet, CSnakes), DotNetPy is built for where .NET is heading: file-based apps, AOT compilation, and declarative dependency management via uv. No Source Generators, no heavy runtime — just inline Python execution with clean data marshaling. This has been a passion project born from real-world needs at the intersection of .NET and AI/data science workflows. I'd love your feedback. 🔗 https://lnkd.in/g23PEys8 #dotnet #python #opensource #nativeaot #csharp

Shipping something useful for everyday Python workflows 🐍 My Python Developer Toolkit - 5 Script Pack is live on Codester, featuring tools for log analysis, file organisation, port scanning, .env validation, and mock REST APIs. Built to be simple, practical, and dependency-light with the standard library only. 🔗 Check it out here: https://lnkd.in/ggAzvx3e #Python #PythonDeveloper #SoftwareEngineer #Programming #Coding #Developer #Developers #TechCommunity #Automation #Scripting #API #DeveloperTools #DevTools #Productivity #Code #ProgrammerLife #BuildInPublic #IndieDev #PythonScripts #TechInnovation #Codester #CodeMarketplace #DigitalProducts #ScriptPack #PythonTools #IndieMaker

How I Combine Different Python Tools in Testing: In real projects, I’ve found that using a single approach is often not enough to ensure quality. Instead, I combine multiple techniques depending on the problem: • Requests (Python) → For validating API responses and backend logic • Selenium → For automating user flows and UI behavior • BeautifulSoup → For extracting and validating UI data against API responses For example: 👉 Validate data via API using Requests 👉 Capture the same data from UI using Selenium + BeautifulSoup 👉 Compare both to ensure consistency across layers This approach helps catch issues that are often missed when testing API or UI in isolation. It’s not just about automation - it’s about validating the system end-to-end with the right tools. How do you ensure data consistency between API and UI in your testing? #qaautomation #softwaretesting #apitesting #python #testautomation #webscraping