Systems | Development | Analytics | API | Testing

Exploratory testing is an essential tool in any comprehensive software testing strategy. If regular scripted testing follows clear directions on a map, exploratory testing ventures off the beaten path to uncover hidden defects. This guide will teach you what exploratory testing is, why it’s critical, how to conduct exploratory testing sessions, and techniques to maximize your results.

According to BMC research, 90% of development managers believe that automating a large number of test cases may become the key to their success as the pressure on IT to accelerate innovation intensifies. However, automating testing processes involves an initial investment, and the benefits can vary between teams driven by multiple forces including the application architecture, number of platforms, and the automation technology choices.

This article is part of a mini-series showcasing how Countly serves various industries. If you're interested in exploring product analytics from different market perspectives, these articles might be helpful: This isn't our first discussion about the crypto market in relation to product analytics and data privacy. While we’ve previously explored topics like NPS and crash analytics, in this article, we'll take a more comprehensive approach, addressing the following questions.

For organizations that generate large amounts of data, implementing a cloud database solution is a critical step towards enabling performant and cost-effective data storage, transformation, and analytics. Choosing the right cloud database solution involves careful consideration of features, capabilities, costs, and use cases to ensure alignment with your organization’s needs and objectives. This blog post features an in-depth comparison of four popular cloud database solutions: Databricks vs.

Live testing is to test software in real-world situations with real users instead of simulated data. This approach allows testers to check software quality in actual usage scenarios and real-world conditions. They do have their own risks to take into consideration, since we are interacting with real users and live systems here. In this article, we will explore the concept of live testing in-depth, and how you can better do live testing with the right tools and approach.

I’m brand new to writing KIPs (Kafka Improvement Proposals). I’ve written two so far, and my hands sweat every time I hit send on an email with ‘ KIP’ in the title. But I’ve also learned a lot from the process: about Apache Kafka internals, the process of writing KIPs, the Kafka community, and the most important motivation for developing software: our end users. What did I actually write? Let’s review KIP-941 and KIP-1020.

3. Elevate risk governance to the next level. NatWest is the UK's leading business bank and second-largest retail mortgage provider. Lengthy change implementation processes hindered risk governance at the bank. Initiating change projects or launching products and applications meant navigating layers of policy checks and approvals, which could take up to 73 days.

Customer experiences matter. And many businesses struggle to improve them. More often than not, a bad process is to blame: manual tasks, data spread across too many systems, or even just old ad hoc workflows that linger on because that’s how it’s always been done. But the cost of ignoring these issues is high.

On April 24, we released Snowflake Arctic with a key goal in mind: to be truly open. In line with that goal, the Snowflake AI Research team is writing a series of cookbooks to describe how to pretrain, fine-tune, evaluate, and serve large-scale mixture-of-experts (MoEs) such as Arctic.