Systems | Development | Analytics | API | Testing

As enterprises double down on AI, there’s a hard truth many leaders are starting to face — they’ve invested in the promise of AI, but they can’t always trust the data behind the predictions. Whether you're training a model, building RAG pipelines, or scaling intelligent automation, AI outcomes are only as reliable as the data feeding them. Yet most organizations still can’t answer a critical question with confidence: Is our data truly AI-ready?

If you’re leading a data, analytics, or AI initiative right now, you know the pressure. AI is no longer a future project - it’s a business imperative. Executives want results, boards want differentiation, and the window to deliver is closing fast. That’s why Salesforce’s intent to acquire Informatica should raise serious questions for data leaders. Not just because of what it means for Informatica, but for what it could mean for your AI roadmap.

Artificial Intelligence(AI) has recently become a hot topic across industries transforming sectors like finance, healthcare, education and research. The two of its subfields are Generative AI and Machine Learning(ML), but both of these terms are often confused for one another. we will explore the difference in purpose, techniques and capabilities and tools like Keploy’s GenAI-powered testing platform makes big difference in software testing.

We previously explored how Codemagic CLI tools can streamline iOS app deployment to App Store Connect with GitHub Actions. The Codemagic team has continuously improved these tools, extending their capabilities to Android releases on Google Play Console.

In our recent webinar, we tackled a familiar frustration for many engineering teams: long build times. Our expert panel broke down the problem and explored how remote build caching can deliver faster builds, improved workflows, and real business value. Here are the five biggest takeaways.

Sometimes the biggest opportunities come disguised as unproven protocols released on a random Monday. Here’s why we bet on MCP before anyone asked us to. ‍ Two months before anyone knew what MCP was, we made a bet that it would fundamentally transform how people interact with their data infrastructure.

AI Needs All Your Data. Your ETL Vendor Is Charging You to Keep It Locked Away. In this post, we explore why legacy ETL pricing models are fundamentally misaligned with the demands of modern AI workloads.

In the last decade, AWS has redefined how businesses build data pipelines. Its ETL toolset isn’t just about moving datasets, it’s about orchestrating security, compliance, scale, and efficiency. Whether you're migrating legacy data systems or building modern ELT workflows, AWS offers a robust, versatile stack of services to meet virtually any requirement.