Systems | Development | Analytics | API | Testing

Retesting is the process of verifying that an old defect has been fixed and the affected features now work properly. It ensures that the same bug does not persist after a fix.

“What am I paying you for, again?” — It’s the question no marketer wants to hear. Consistent, insightful reporting is crucial for client satisfaction, but let’s face it: juggling spreadsheets and Google Slides isn’t cutting it anymore. In 2025, clients expect professional, data-driven presentations. But with countless expensive reporting tools flooding the market, where should you invest your hard-earned money? This year, I put several popular options to the test.

Mobile app developers are obsessed with speed for one simple reason - their customers are too! We expect a lot from the apps we use. They've got to load super fast, always stay up to date, and offer a steady stream of valuable features. For developers, this means relentless pressure to reduce build times and drive efficiency to stay competitive.

DevOps has revolutionized how teams build, test, and release software, emphasizing speed and collaboration. However, with rapid development cycles and continuous deployments, one major challenge remains: maintaining full traceability.

Managing and integrating data efficiently is a critical requirement for businesses dealing with multi-source, real-time, and large-scale datasets. Google Data Management provides a scalable, cloud-native ecosystem designed for seamless data integration, transformation, and governance. This blog explores Google’s data integration solutions, including ETL/ELT pipelines, real-time data streaming, and AI-powered automation for enterprise-grade data workflows.

In the modern data-driven landscape, enterprises require robust platforms to handle massive volumes of structured and unstructured data efficiently. Databricks, a unified analytics platform built on Apache Spark, has emerged as a leader in data management, offering high-performance computing, collaborative workspaces, and seamless integrations with cloud providers. In this blog, we explore how Databricks transforms data management, its core components, and best practices for optimizing data pipelines.

Artificial Intelligence (AI) is transforming the way businesses manage, process, and analyze data. AI Data Management involves the use of machine learning (ML), automation, and intelligent data pipelines to enhance data storage, governance, integration, and security. As organizations deal with ever-growing datasets, AI-driven data management solutions ensure efficiency, scalability, and accuracy.

Measuring DevOps success? You need to use DORA metrics from the DevOps Research and Assessment (DORA) framework. These metrics help software teams get better over time. Think of DORA metrics like the stats used in Formula One racing. In Formula One, teams collect and analyze thousands of metrics before and during each race to optimize car setups, detect potential failures early, and refine strategies for peak performance.

As the need for speed in business can seemingly be at odds with the need for control, organizations developing APIs today face a critical challenge: how can you empower developers to build and deploy APIs quickly while maintaining enterprise-wide governance and security? More traditional API deployment approaches are often to blame for why API initiatives fail to deliver the promised benefits as complexity and scale increase.

I recently saw a post on LinkedIn that said, “Qlik isn’t the same company it was in 2016.” I’m pretty sure that it wasn’t meant as a compliment. But here’s the thing: they’re right. And that’s a good thing. Because if we were the same company we were in 2016, we wouldn’t be prepared for the challenges businesses are facing today. The world of data and AI has changed. Businesses have changed. So, of course, Qlik has changed too.