Just shy of a year ago and coinciding with the Atlassian Team ‘23 conference, Testlio unveiled an initiative to help product teams adopt signal-driven testing as a core pillar of the future of software quality engineering. A lot of exciting things have happened and continue to happen since that announcement, which collectively serves as validation of the opportunity for product teams to dramatically improve test coverage efficiency through signal-driven testing.

As organizations seek to drive more value from their data, observability plays a vital role in ensuring the performance, security and reliability of applications and pipelines while helping to reduce costs. At Snowflake, we aim to provide developers and engineers with the best possible observability experience to monitor and manage their Snowflake environment. One of our partners in this area is Observe, which offers a SaaS observability product that is built and operated on the Data Cloud.

Let's talk about something that might not be your favorite topic but is super important: data privacy and security. Now, I know it might sound like just another box to tick off, but hear me out. Ignoring data privacy in today's digital world is like forgetting to lock your doors in a busy neighborhood. Not the best idea, right? We previously discussed the importance of data privacy in analytics, let’s now look at the implications of lack thereof.

Build your own generative AI chatbot! This post is a continuation of our earlier blog post, “Assembling a RAG architecture using Fivetran.”

In this age of AI, the demand for real-time data integration is greater than ever. For many, these data pipelines should no longer be configured and deployed by centralized teams, but distributed, so that each owner creates their flows independently. But how to simplify this, whilst practicing good software and data governance? We are introducing Lenses 5.5.

Have you ever wondered how to track events in a large codebase? I gave it a shot using Apache Kafka®! Read on to learn how to use GitHub data as a source, process it using a Kafka Streams topology, and send it to a Kafka topic.

Stop me if you’ve heard this one before, but there’s a lot of data out there — and the amount is only growing. Estimates typically show persistent data growth roughly at a 20% annual compounded rate. Capturing, storing, analyzing, and actioning data is at the core of digital applications, and it’s critical for both the day-to-day operations and detecting trends, for reporting, forecasting, and planning purposes.

Appium Inspector integrated support in Sauce Labs Real Device Cloud, using the latest version of Appium, now allows you to inspect Appium locators in real-time for native applications.