Systems | Development | Analytics | API | Testing

How do you scale testing while keeping up with a fast-growing product? @At BlaBlaCar, Quality Assurance Manager @Rémy Gronencheld introduced a “Q-as-a-Service” model that redefines how QA operates. Instead of a one-size-fits-all approach, the QA team adapts to the company’s needs—whether that means automating tests, mentoring teams, or optimizing workflows. By creating flexibility, Q-as-a-Service ensures quality becomes everyone’s responsibility while still providing expert guidance where it’s needed most.

Helix Swarm is a free code review and collaboration tool from Perforce, makers of Helix Core, the industry-standard version control platform. Optimize Your Code Review Process Join Senior Solutions Engineer Jackie Garcia as she shows you how to request, approve, comment, and upvote a Swarm review. These reviews streamline your coding process, making it faster, smoother, and way more efficient. Watch the video and see how Helix Swarm lets you.

Snowflake provides for seamless handling of geospatial data, making it easier to work with location-based information directly in your data platform. In this video, we explore Snowflake’s native support for geospatial data, which allows you to store, process, and analyze spatial information at scale. Geospatial data is important because everything happens somewhere. By breaking down silos and combining spatial and non-spatial data, Snowflake empowers you to uncover valuable insights across a wide range of use cases —from mapping to location analytics to geospatial trends.

How Low-Code/No-Code Platforms Are Shaping the Future of Test Automation? Gokul Sridharan emphasizes that these platforms have three types of impact on organizations upon adoption.

Automation is like slicing through butter—smooth and time-saving. But how can you maximize your results without driving your developers up the wall? @Lori Henderson shares how they’ve boosted first-time releases and decreased hot fixes by blending automation with peer testing. Trust us, less manual work + quality throughput = happy developers.

Shift-left testing gets all the attention, but it’s shift-right that reveals what truly works. At @BlaBlaCar, Quality Assurance Manager @Rémy Gronencheld explains why testing in production is critical for real-world success: Shift-left: Build with confidence but rely on assumptions. Shift-right: Test against the unpredictable—low connections, real devices, and user behavior. The reality? Combining both approaches lets teams take calculated risks without sacrificing quality.

Snowflake Data Clean Rooms empower organizations to collaborate on data in a privacy-conscious way directly within Snowflake. With an intuitive interface and a focus on simplifying secure data sharing, Snowflake Data Clean Rooms enables businesses to build and use clean rooms seamlessly, leveraging Snowflake’s powerful data platform. This solution eliminates unnecessary complexity and additional access fees, ensuring organizations can focus on deriving insights while maintaining data privacy. Learn more about how Snowflake Data Clean Rooms support privacy-preserving collaboration in this blog.

Is your QA strategy keeping up with the speed of innovation? In this episode of Test Case Scenario, Jason Baum and Marcus Merrell are joined by Rémy Gronencheld, Quality Assurance Manager at BlaBlaCar, to explore how the global carpooling platform scaled from manual testing to seamless automation. Rémy dives into BlaBlaCar’s journey—moving from Mac minis and manual regressions to a robust, scalable test automation suite with Sauce Labs.

Here's the collaborative creator space data teams have been waiting for. Harness the power tools you need — SQL, R, Python, and visual analysis — all in a seamlessly integrated intelligence platform.

The AI explosion has led to non-stop hype cycles as the technology continues to develop. But AI is only as good as the data behind it. The threat of lousy data is bad AI. Andrew Brust, Founder and CEO of Blue Badge Insights, joins The AI Forecast to discuss the AI hype–and how to prevent what he calls an “artificial Charlatan” of bad AI. He emphasizes the dependent relationship between data and AI and the former’s role in the success of the latter. Specifically, he addresses the data governance conundrum, and why in order for data technology to be successful, it has to be fun.