Systems | Development | Analytics | API | Testing

Users of Snowflake and other data lakes and data warehouses need real-time data for artificial intelligence (AI) and analytical workloads—but they struggle to get that data into their lakes and warehouses. In response to this ubiquitous challenge, Confluent developed Tableflow.

Outsourcing quality assurance (QA) to a high-quality software testing service can be a good way to perform more testing at a low cost while freeing developers from test creation and maintenance. However, through surveys and interviews with numerous engineering leaders, we found that not all software testing services can deliver on these benefits. Many are notoriously slow and struggle to catch obvious bugs in critical user paths.

At the top of many IT folks’ minds lies the looming 2027 deadline for SAP’s end-of-life for SAP ECC – and the subsequent migration to S/4HANA. Often a complex and lengthy migration process, the shift to S/4 requires careful planning and resource allocation.

In today’s world, almost everyone has a mobile device full of apps. Most commonly, mobile apps serve as essential tools for personal and professional communication. However, the scope of apps goes much further, since many individuals use apps for fitness, healthcare, shopping, entertainment, and so much more. This means that, while your phone and its apps are packed with convenience and efficiency, they also come with one large hidden cost: your personal data.

Performance and stress testing are two popular testing methodologies. They focus on determining how an application behaves under regular and unexpectedly high traffic, respectively. These tests are essential for assessing a system's robustness and scalability, particularly in applications with fluctuating traffic like Node.js backends. In this article, you'll learn about stress and performance testing, why they're important, the best tools available, and how to use them in Node. Let's dive in!

Generative AI models are not just tools for producing text, audio or video—they're systems that learn patterns, improvise, and generate unexpected outcomes. When we look at LLMs, we're struck by their capacity to generate surprisingly creative and context-aware results. They can weave coherent narratives, propose novel solutions, mimic human conversation, and even offer nuanced insights across a wide range of topics. While this creativity is their strength, it also introduces variability and risk.

In 2025, QA teams are experiencing real change. Product delivery cycles are becoming shorter, Agile is maturing, and there’s increasing pressure to launch new software quicker and without flaws. There’s also evident talk surrounding AI as the biggest factor for change, and its impact is also increasing. But the truth is AI is just one factor in this story. Other trends - like continuous automation, DevOps integration, and the role of QA - are redefining the way we test and ensure quality.

Retailers must use flexible, scalable architectures to integrate diverse, fragmented data sources and create better customer experiences.

In the field of software quality assurance, there are two types of testing often referenced, smoke testing and regression testing. While they are both vital to software quality, each has its own unique functions and overlaps in the software development cycle. This post explores the differences between smoke testing vs regression testing, and when and how to effectively implement each.

How we architected a responsive, HTTP-aware scale-to-zero mechanism to improve resource efficiency across cloud native workloads in Choreo. Cloud platforms promise elasticity, but achieving true efficiency, especially for idle or sporadically used services, requires more than just horizontal scaling. At Choreo, we set out to rethink how workloads behave when they're not actively serving requests.