Systems | Development | Analytics | API | Testing

How ThoughtSpot Fixed This CIO's Biggest Headache

The secret to a seamless customer experience? Embedding your intelligence. Ligentia wanted one consistent, branded experience across their entire supply chain offer. The fix? Partnering with ThoughtSpot. Catch Ligentia CIO Boris R. and Cindi Howson on podcast discussing how to turn standard apps into data powerhouses. Music: “The Clermont” by Flash Fluharty Licensed via PremiumBeat, ID: P9IHFMDYNZCKLEFZ.

Introducing Releases in Appian: Organize, Deploy, and Deliver with Confidence

As enterprise development teams scale, coordinating deployments across multiple teams, applications, and environments becomes one of the most time-consuming parts of the delivery lifecycle. Today, we're excited to introduce Releases—a new capability in Appian that brings native release management to the platform, helping teams deploy faster and with fewer surprises.

Architectural Decision Guide: When to Use Apache Kafka (And When You Shouldn't)

Your team just shipped a microservices refactor. Services are smaller, deployments are faster, and boundaries are clearer. Then, during a design review, someone inevitably suggests: “We should use Kafka.”That suggestion might be the exact architectural breakthrough you need—or it could quietly introduce months of unnecessary operational complexity.This article serves as a practical decision framework.

Cypress vs Playwright vs No-Code Testing: Which Is Right for Your Team?

If your team is evaluating browser test automation, there’s a good chance the conversation starts with Cypress vs Playwright. Both tools have earned their popularity. Playwright is widely used by engineering teams that need reliable end-to-end testing, cross-browser support, and strong CI/CD integration. Cypress remains a favorite among frontend developers who want an interactive testing experience, fast local feedback, and approachable debugging tools.

Agentic Workflow for Petabyte-Scale Data Analytics | Cloudera Agent Studio

Struggling to get clear, reproducible insights from petabytes of data? Join Charu Anchlia, Principal Engineer II at Cloudera, to see how Cloudera Agent Studio brings business users and tech analysts together under one simple interface. See how multi-agent orchestration—using specialized SQL and coding agents—can solve complex data analysis challenges, generate real-time visualizations, and seamlessly transform LLM outputs into repeatable Airflow pipelines.

Build Your Super Team: What 150 Years of Soccer Data Says

Soccer is a game of stories, but the most fascinating stories are often buried deep inside the numbers. And this year on the world's biggest stage, the tournament has expanded by nearly 60% – traditional scouting reports and pundit hot-takes simply can't keep up with the sheer volume of new data. That’s why we’re looking at the tournament through a much wider lens.

WSO2 Appoints Tanmaya Das as CFO to Support Continued Global Expansion

London, UK. 11th June 2026 - WSO2 today announced the appointment of Tanmaya Das as Chief Financial Officer (CFO), further strengthening its executive leadership team as the company continues to build on its momentum and position itself for its next phase of growth. The appointment comes as WSO2 expands its platform strategy for the agentic enterprise, helping organisations securely integrate AI capabilities into complex technology environments while maintaining control over governance, data sovereignty, and digital infrastructure.

Human in the Loop Testing: Where AI Ends and QA Judgment Begins

The question isn't whether to use AI in QA. It's knowing exactly where to keep a human in control. The core risk: Over 75% of multi-agent failures are silent semantic errors that pass automated checks but violate business logic — detectable only by human inspection (Cemri, Pan et al., NeurIPS 2025). The division of labor: AI owns repetitive generation and execution; humans own risk analysis, requirement interpretation, exploratory investigation, and final sign-off. The operational discipline.

Generative AI for QA: How SDET Workflows and Skills Are Changing

Generative AI for QA is the use of large language models to accelerate the creation and analysis of testing artifacts — drafting test cases, summarizing requirements, and generating synthetic test data. AI agents extend that capability into multi-step autonomous workflows that plan, delegate, and execute testing tasks across an entire delivery pipeline. For SDETs, the shift is not about learning to prompt more cleverly.

Temporal made execution durable. Ably makes sessions durable.

When Temporal launched, a lot of people had the same reaction: "We have queues and retries. We don't need this." (Temporal's own blog addressed this directly.) That reaction made sense. Queues solve queue problems and they do it well. What Temporal gave you was something different: a named execution context that survives a server restart and picks up from its last checkpoint. Not a better queue. A different abstraction entirely. If you built with it, you couldn't imagine going back.