Explore mainframe capacity limits and learn how to optimize performance without adding unnecessary resources.

Explore mainframe capacity limits and learn how to optimize performance without adding unnecessary resources.

Thought leadership on preserving legacy system knowledge. Positions modernization as a strategic need, using AI, DevOps, and mentorship to engage B2B audiences and drive conversions.

A ghostwritten blog post highlighting how to stream mainframe data into Kafka securely and without disruption using CONNX, one of Software AG's flagship products.

Blog post and video script that distills the differences between APIs and Webhooks into a digestible and accessible format.

The complexity of the global data privacy landscape presents a challenge for any business that collects and leverages customer data––especially those with a multinational customer base. mParticle simplifies this process by making it easy for

Like data-driven decision making, being data-informed entails relying heavily on raw, measurable information to guide an organization’s direction. Data-informed strategies leave more room for opinions and past experiences, however, and recognize the

Engineers should ship products, not data. Product managers and marketers should experiment with data, increase personalization, and improve experiences. With a permanent data infrastructure, these goals are not mutually exclusive.

Once upon a time, building a mobile application meant using programming languages specific to the environment where that app would live. Now, it’s not so simple. Here’s a breakdown of the options and tradeoffs that engineering and product teams need

"Wait, why do we need this data again?" "Was that attribute supposed to use snake or camel case?" Data tracking plans keep everyone in your organization aligned on your data efforts, from the high-level strategy to the nittiest, grittiest details.

AI-driven insights are powerful catalysts for supercharging your customer data. But all too often, these resources are siloed in data science departments, disconnected from go-to-market teams who possess the skills to maximize their value. This

Learn the differences between a CDP vs data warehouse, and how you can use both in tandem to configure an architecture that makes sense for your data and business needs.