The open-source philosophy and principles have always been about the democratization of knowledge—giving everyone the opportunity to access, learn, modify, and share software freely. As we see AI technologies seemingly taking over our day-to-day lives, we need those benefits in AI as well. Open-source AI inherits the open-source principles. It fosters transparency and collaboration, allowing us to build robust and secure AI systems.

In today’s regulatory environment, organizations are compelled to adopt stringent security measures to mitigate the risk of PII (Personally Identifiable Information) data breaches in their production environments. However, the pressure to provision more test data in non-production environments, often sourced from production data, has introduced new vulnerabilities.

I’m happy to share that Snowflake has signed the Cybersecurity and Infrastructure Security Agency (CISA) Secure By Design Pledge as we champion the advancement of industry standards for security in technology design. The CISA pledge to foster tech-ecosystem security deeply aligns with Snowflake’s own product design ethos, where security is built in from the start.

We are proud to announce the release of Apache Kafka 3.8.0. This release contains many new features and improvements. This blog post will highlight some of the more prominent features. For a full list of changes, be sure to check the release notes. See the Upgrading to 3.8.0 from any version 0.8.x through 3.7.x section in the documentation for the list of notable changes and detailed upgrade steps. In a previous release, 3.6, Tiered Storage was released as early access feature.

Cloudera’s data-in-motion architecture is a comprehensive set of scalable, modular, re-composable capabilities that help organizations deliver smart automation and real-time data products with maximum efficiency while remaining agile to meet changing business needs. In this blog, we will examine the “why” behind streaming data and review some high-level guidelines for how organizations should build their data-in-motion architecture of the future.

Idempotent operations produce the same result even when the operation is repeated many times. The result of the 2nd, 3rd, and 1,000th repeat of the operation will return exactly the same result as the 1st time. In this post, we will demystify the concept of idempotency—a fundamental property that ensures consistency, predictability, and reliability in APIs and distributed systems.

Modern data platforms enable enterprises to extract valuable business insights from data, sourced from various origins. Data engineers, data scientists, and other data practitioners utilize both data streaming and batch processing frameworks as a means to provide these insights. While batch processes work on historical data, stream processing extracts insights in real time, enabling businesses to react faster with respect to changing events.

Test automation is no longer a cutting-edge technology. It is now a standard for any QA team looking to bring product quality to the next level. However, some testers are hesitant. They pose questions: does test automation actually bring ROI? Or perhaps they’re wary of the struggle with maintaining automation test scripts that break every time a new update is rolled out. It is those questions that deter business leaders from making the final decision, but they have a reason to be wary.

At Ably, we provide a realtime messaging service that aims to deliver messages between globally-distributed participants with the lowest latency possible. When powering apps that enable realtime collaboration, or require time-critical updates, low and consistent latency is essential.