Systems | Development | Analytics | API | Testing

Growing business demands require enterprises to manage a vast number of APIs. The volume and complexity of these integrations make maintaining quality a challenge. To address this, governing the API lifecycle—from design to retirement—has become essential. API management vendors are therefore developing tools and frameworks to assist.

Apache Iceberg is transforming how organizations build and manage their data infrastructure, enabling lakehouse architectures that combine the best of data lakes and data warehouses. In this blog, we look at five real-world implementations demonstrate Iceberg's versatility and the advantages it brings to modern data management challenges. Learn more about Data Lakehouses.

Android app development is the process of building software for Android devices, such as smartphones, smart TVs, tablets and wearables. It’s a Linux-based system and open source, which means manufacturers can customize it. Android version names used to be named after desserts, like Lollipop, Marshmallow, KitKat and Oreo. From version 10, Google switched to number-based names like Android 10, Android 11, Android 12, etc, up to the most recent, Android 15, which was released in September 2024.

Service level agreements (SLAs) are the contractual backbone between customers and technology vendors, outlining expected service availability, performance metrics, and remedies like service credits when service providers fail to meet agreed-upon service levels. This service agreement assures both the technical quality as well as the service quality of the services provided, and underpins the value perspective of the client.

Your brand isn’t just a logo anymore. It’s a promise that lives across dozens of digital touchpoints your customers encounter daily. Whether you’re manufacturing industrial equipment or consumer goods, every product detail, specification, and image becomes part of your brand story. And here’s the thing: if you’re not controlling that story, someone else is writing it for you.

Bugs don’t wait, and neither should your testing tools. As software grows smarter and faster, the race is on for solutions that can keep pace, efficient, dependable, and ahead of the curve. Open source testing tools are now an essential component of organizations (especially startups) that need to automate their quality assurance process without spending a hefty amount on testing.

The complexity of software continues to increase as teams adopt microservices, APIs, and cloud-native architectures. Manual testing is no longer able to keep up with the speed of continuous releases. QA teams are dealing with increasing pressure to not only assure software quality but to do so in a shorter cycle time. This is where AI Testing Tools can provide a solution. AI-powered testing takes advantage of machine learning, predictive analytics, and self-healing features.

Struggling with secure, real-time enterprise data access? RAG (Retrieval-Augmented Generation) systems are popular but often fall short in handling dynamic data, security, and compliance. Enter agent-based systems - designed to securely connect AI to live databases, APIs, and ERP systems while enforcing strict permissions and audit trails. Key Takeaways: RAG systems lack granular security, real-time updates, and detailed compliance tracking.

A data contract is a formal agreement between an upstream component and a downstream component on the structure and semantics of data that’s in motion. In a previous post, I showed how Confluent Schema Registry supports data contracts. By combining data contracts and encryption on streaming workloads, you can shift left the responsibility of data consistency, quality, and security to the producer, allowing the consumer to depend on a trustworthy stream of data.