‍ They started in awe, which soon turned into desperation to keep up, and it is only now that we have started realizing the utility and business value of our Artificial Intelligence (AI) goals. I like this stage of our industrial revolution, where we are no longer expecting magic from AI but are integrating it nevertheless for all the wonders it can still do for our businesses. This was the same space where our DevOps efforts started yielding enterprise-level transformations.

The stage is set for a new era in marketing, and marketers have never had so much data and technology at their fingertips. But to deliver the ROI that enterprises require today, marketers must have a strategic mindset and fine-tune the tools, tactics and approaches in their marketing data stack. Snowflake is here to help marketers evolve and accelerate their marketing impact with our third annual Modern Marketing Data Stack report and global virtual event.

One method that developers use to submit code modifications for evaluation is known as pull requests (PRs). We discussed the advantages of testing PRs in software development in a previous post but before merging into the main branch, they give team members a chance to talk about the changes, make suggestions, and accept or reject the code. To keep code quality high and make sure that the team reviews all changes, this approach is essential.

BigQuery data preparation is an AI-first solution that streamlines and simplifies the data preparation process as part of Gemini in BigQuery.

The GenAI revolution is well and truly here. To take inspiration from our favorite comfort show, Gilmore Girls, “It’s GenAI’s world, and we’re just living in it.” In fact, McKinsey reports the number of organizations regularly using GenAI has doubled in ten months between their 2023 and 2024 surveys.

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both operational and analytical use cases. Streams provide low-latency capabilities to enable timely reactions to events, while tables provide higher-latency but extremely batch-efficient querying capabilities. You simply choose the most relevant processing head for your requirements and plug it into the data.

Dynamic real estate market requires businesses to act fast and effectively. Having software that can automate most of your business and management-related tasks is a wise choice. When it comes to deploying a real estate analytics platform, you need to decide between two approaches — custom software and ready-made solutions.

All developers will eventually need to integrate an app with a third-party REST API when doing web development. However, the RESTful APIs that we are trying to make sense of often do not provide a lot of flexibility. This article addresses this issue within your codebase, showing you how to seamlessly implement any Laravel API integration.

Your docs are a lot like your family—not in the corporate jargony “we are a family” way, but more in the “can’t live with them, can’t live without them” way. Yes, these docs are crucial in more ways than one, but teams that regularly work with them know that the time they spend searching for, cleansing, and prepping their docs can be better utilized elsewhere.

Imagine your development team just rolled out a new feature that fixes a bug but now some parts of your software that used to work flawlessly have stopped working. This is a common risk in Agile development, where changes come fast and often. Since Agile moves quickly with constant updates, regression testing is a key part of making sure the software stays reliable. With each new update, there’s a risk that changes might impact parts of the software that were already working well.