Systems | Development | Analytics | API | Testing

Your AI Project Has a Data Liberation Problem

Generative AI has the potential to add up to $4.4 trillion annually to the global economy. But most organizations won’t see that value—not because of their models or infrastructure, but because of their data. Despite years of investment in data lakes, warehouses, and analytics tools, organizations are drowning in complexity. Data is scattered across siloed systems, riddled with duplication, and locked behind outdated batch processes.

Spark NZ Sets Secure, Governed Data Foundations For The Era Of AI

Over the past few years, Spark New Zealand has tackled the challenge of creating a strong data foundation by moving all of its data warehouses into Snowflake to create a centralised data platform. Now, explains Pritha Chattopadhyay, Domain Chapter Lead at Spark, this telecommunications leader and digital services provider is diving into artificial intelligence with the help of Snowflake Cortex AI. Tune in to learn about the benefits it provides.

Automated Cost Management: Leveraging AI for Databricks Optimization

Accurate forecasting of cloud costs remains a significant challenge for 80% of data management experts (Forrester). The root causes? Lack of granular visibility, siloed data, and the absence of AI-powered predictive tools. Join us for this session in our Weekly Walkthrough drop-in series, "Controlling Cloud Costs," where we'll explore how to manage Databricks costs with AI.

AI for Software Engineering Forecasting

New AI and Machine Learning (ML) solutions have become one of the most powerful tools in today’s technology stacks. These support the ability to process and analyze vast amounts of data to identify patterns and to make forecasting more reliable. AI has been driving innovation in healthcare, finance, and retail. Now, it's beginning to revolutionize the field of software engineering. This article shares some of our innovations in applying AI to software engineering processes.

AI-Powered Sales Assistant: The Future of Sales Productivity

Sales reps dedicate just two hours each day to active selling, according to HubSpot research. At Snowflake, our sales team found they were wasting 10 to 15 minutes searching for the right content every time they needed to answer a single question, like “Can you explain how Snowflake handles data integration from various sources?” Valuable content was scattered across different platforms, forcing employees to hop between various tools to assemble the right information.

Smarter AI Adoption

AI promises efficiency, but are we implementing it the right way? @Marcus Merrell shares what’s critical to track AI usage and its impact: “Here’s the prompt I used to get this tool, and here are the changes I made to make it work.” This kind of transparency is non-negotiable. Start small with a group of mixed experience levels to uncover both benefits and risks before scaling. If AI adds overhead without solving core issues, is it truly worth the investment?

Is AI Falling Short of Expectations?

AI tools like Copilot and ChatGPT promised to revolutionize development workflows, but are they delivering or just creating new headaches? The stats speak volumes: 92% of developers say AI increases the blast radius of bad code 67% are spending more time debugging AI-generated code 59% face deployment errors at least half the time when using AI tools So, are we making strides toward innovation or spinning in circles of hype? @Marcus Merrell put it best: “This stuff was supposed to already start paying off by now. So why isn’t it working?”