Systems | Development | Analytics | API | Testing

The Fivetran binary log reader optimizes change data capture for superior performance and lower impact when replicating from SQL Server.

Companies are rapidly adopting AI, but it's not all roses. The excitement comes with significant risks, such as shadow AI, runaway costs, and security nightmares. This post explores the real challenges organizations face in AI governance today and highlights how forward-thinking companies are beginning to tackle them. Companies are charging headfirst into AI, with research around agentic AI in the enterprise finding as many as 9 out of 10 organizations are actively working to adopt AI agents.

The messaging and data streaming industries are constantly evolving, with major shifts such as large vendor purchases. If your company relies heavily on a platform such as Apache Kafka and you recently discovered that your vendor was acquired by a competitor, you are most likely dealing with a combination of uncertainty, budgetary risks, and technical difficulties.

In today’s fast-paced software environment, getting product features out the door quickly is the minimum. Getting features out the door quickly + with reliability is what separates high performing teams opening up larger opportunities. This is where DevOps testing comes into play, testing not just at the endpoint of the development + operations lifecycle but as an integrated process throughout every step of the life of the product.

In today’s product landscape, analytics isn’t just about showing insights. It’s about moving data into the tools where work actually happens, so that users can make informed decisions in context. Users expect analytics to integrate with their systems, whether that means sending a lead to a CRM, creating a Jira ticket, or triggering a workflow downstream. As product owners, that means one thing: your analytics experience should enable seamless data flows across systems.

A comprehensive guide to DreamFactory's evolution from a monolithic platform to a modular, container-ready architecture—and how administrators should approach version upgrades.

Gartner has once again named Qlik a Leader in the Magic Quadrant for Data Integration Tools, a position we have held for a decade. In that time, the landscape around data integration has shifted. Hyperscalers are moving up, large vendors are tightening their stacks, and acquisitions are reshaping customer choice. For CIOs and CDOs, that consolidation changes the question. It is less about who sits where in the quadrant, and more about how much control you still have over your own data and AI strategy.

Most AI analytics projects are failing to deliver on their promises, and the cause isn’t what you might expect. This creates widespread project failures and undermines confidence in AI-driven analytics. What are the problems with AI analytics and how can organizations address them?

When you embed traditional BI tools, you work with platforms originally designed for internal analysts who expect to explore data directly. Embedded capabilities came later, and while these tools expose APIs, every variation in experience requires development work. The challenge is that traditional BI tools aren’t built for the full spectrum of embedded use cases. Most embedded analytics implementations must serve several distinct user types inside your customers’ organizations.

CWE and CWE Top 25 secure coding practices help you safeguard your code against rising software security risks. Here we explain what is CWE and the CWE Top 25. Read along or jump ahead to the section that interests you the most.