Systems | Development | Analytics | API | Testing

Python Observability : A Complete Guide

Observability is a critical element of modern software development, unlocking awareness across complex and distributed systems with ease. This has allowed developers to monitor, understand, and debug their applications effectively, leveraging existing resources for more efficient lifecycle management and iteration. In the context of Python, observability is an engine for boosting and maintaining the performance, reliability, and stability of the implementation.

Why Selenium isn't a testing framework

There’s a better way to approach test automation. Selenium is a powerful browser automation tool, but it’s not a testing framework—and that distinction matters. @Diego Molina from Sauce Labs discusses why comparing Selenium to testing framework tools like Playwright or Cypress is like comparing apples to oranges. While Selenium provides the capabilities for automation, tools like Playwright offer full-fledged testing features. By recognizing the unique strengths of each of them, you can make an educated choice on which tool would best fit your testing strategy.

Invoice data extraction 101: How to extract data from invoices in 2025

Businesses send and receive several invoices and payment receipts in digital formats, such as scanned PDFs, text documents, or Excel files. While digital formats have allowed workplaces to transition to a paperless environment, they have introduced a new challenge for business analysts: extracting the data from invoices and using it to draw relevant insights.

Playwright Alternative For Api Testing

If you’re an SDET who’s been using Playwright for API testing, you might be all too familiar with the frustrations of dealing with database dependencies, data management, and the endless need for cleanup. Let’s face it – Playwright, while fantastic for UI testing, can be cumbersome when it comes to API testing. But what if there’s a better way to handle this?

How to Load Test Kubernetes

Performance tests, end-to-end tests, integration tests. There are many different types of tests you can run on your infrastructure. One of the most overlooked kinds is load testing. Failure to include load tests in your supply chain can be detrimental, as you will be missing out on a number of benefits. Some of the big advantages of load testing Kubernetes are.

Automate Medical Report Processing with Astera in 4 Simple Steps

Processing medical reports from various labs, clinics, and hospitals can be smooth and efficient with Astera. No matter the format or source, Astera simplifies every step of the process. Automatically ingest reports, extract key data fields like patient details, test results, and diagnoses with precision and deliver clean, ready-to-use data straight to your EMR systems, databases, or Excel. With Astera, your team can save time, reduce manual effort, and focus on delivering faster, more accurate patient care.

Revolutionizing Enterprise AI: ClearML and AMD Collaborate to Drive Innovation at Scale

In a significant stride toward transforming AI infrastructure, ClearML has recently announced a collaboration with AMD. By integrating with AMD’s powerful hardware and open-source ROCm software with ClearML’s silicon-agnostic, end-to-end platform, we’re empowering IT teams and AI builders to innovate with ease across diverse infrastructures and integrate GPUs from multiple vendors.

Why parallel test execution is a game-changer

Struggling to scale your tests effectively? It doesn’t have to be complicated, thanks to tools like Selenide. Parallel test executions can be as simple as: Writing your tests, setting a flag, and letting the framework handle the rest. Parallel runs allow you to finish test suites in record time. Reduced bottlenecks and being able to focus on delivering value with tools designed for growth.

Generative AI Meets Data Streaming (Part III) - Scaling AI in Real Time: Data Streaming and Event-Driven Architecture

In this final part of our blog series, we bring everything together to unlock the full potential of AI with real-time data streaming and event-driven architecture (EDA). In Part I, we explored how data fuels AI, laying the foundation for understanding AI’s reliance on fresh, relevant information.

Generative AI Meets Data Streaming (Part II) - Enhancing Generative AI: Adding Context with RAG and VectorDBs

In Part I of this blog series, we laid the foundation for understanding how data fuels AI and why having the right data at the right time is essential for success. We explored the basics of AI, including its reliance on structured and unstructured data, and how streaming data can help unlock its full potential.