Systems | Development | Analytics | API | Testing

The software and product life cycle is fraught with pitfalls and tradeoffs. While testing applications under production-like load is critical to ensuring the reliability, performance, and security of your data storage and software services, you need to do this testing without actually affecting the production data and systems. In essence, you have to pull off the impossible – be as close to production as you can without actually being production.

EU-based organizations, particularly those in regulated sectors such as Financial Services, Energy, and Telecommunications, are required to store and process data strictly within the EU to legally comply with regulatory frameworks such as the General Data Protection Regulation (GDPR).

Delivering analytics quality at a global scale is never easy. One broken event or missed signal can derail product launches, fuel bad decisions, and shatter customer trust overnight.

The AI agent revolution is transforming how we work, but most analytics platforms are stuck in the past—forcing you to context-switch between your agents and separate BI tools to get data insights. This fragmented approach creates friction, breaks workflows, and ultimately slows down decision-making. When speed matters most, you need your AI agents to seamlessly access and analyze enterprise data without the traditional barriers of maintaining custom integrations and limited API functionality.

Imagine you’re closing a big deal with a Fortune 500 client. Everything looks good until they ask, “Are you SOC 2 compliant?” If you can’t answer with confidence, that deal might slip away. By 2025, with a threat landscape that evolves daily and customer trust is challenging to acquire, SOC 2 compliance has become the new baseline for doing business for SaaS, cloud services, fintech, and digital health vendors.

It’s 2 AM. Your release just broke again. CI pipelines were green, tests appeared clean, and the dashboard looked harmless. Yet, staging is down, and no one knows why. You're staring at static HTML reports and generic error logs. They show what failed, but give no clue why, no context, no trace, no help. It's like debugging in the dark. This is what outdated test reporting costs: lost hours, broken releases, and reactive firefighting when you should be sleeping or shipping.

If your organization has a complex Oracle deployment, or you are on a modernization journey — migrating to the cloud, preparing quarterly updates, or consolidating systems, you already know the stakes are high. Oracle systems and transformations are strategic, complex, and costly. But too often, they’re derailed by one silent saboteur: outdated, manual testing.

Regression test cases are a core part of any stable release cycle. They help you confirm that what used to work still works, even after new features are added or bug fixes are applied. But not every regression test case should be automated. Some are too brittle. Some don’t run often enough. Others are simply not worth the maintenance effort. So how do you decide which ones are worth automating? This article will guide you through exactly that.

Filtering events in REST APIs lets you request only the data you need, improving efficiency, reducing server load, and speeding up responses. The process involves using query parameters and operators to define conditions for retrieving specific records, like filtering by date, category, or status. Here's the core idea: Query Parameters: Add key-value pairs to the URL (e.g., ?date=2022-03-01) to filter events by specific fields.

Operational and analytical estates have been separated since data warehouses were first introduced in the 1990s. The operational estate includes microservices, software-as-a-service (SaaS) apps, and enterprise resource planning systems (ERPs) that have become the beating heart of an organization. The analytical estate consists of the data warehouses, lakehouses, artificial intelligence (AI)/machine learning (ML) platforms, and other custom batch workloads that support business analysis and reporting.