Systems | Development | Analytics | API | Testing

Key Takeaways from Accelerate: How Financial Services and Manufacturing Companies Leverage Data and AI for Measurable ROI

For many organizations across industries, the era of experimental AI has given way to the era of practical implementation. Even those companies still testing and evaluating AI solutions are shifting away from the art of the possible to focus more closely on what will soon produce measurable ROI. “It will no longer be enough for your organization to merely use AI to win the approval of company leadership,” says Samuel Lee, Product Marketing Director for Financial Services at Snowflake.

The Apache Iceberg Avalanche: How the Open Table Format Changes the Face of Data Lakes

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems.

Break Data Silos: Build, Deploy and Serve Models at Scale with Snowflake ML

Despite the best efforts of many ML teams, most models still never make it to production due to disparate tooling, which often leads to fragmented data and ML pipelines and complex infrastructure management. Snowflake has continuously focused on making it easier and faster for customers to bring advanced models into production.

Scale Your Python Analytics With Pandas On Snowflake

Massive data sets can overwhelm native Pandas, causing memory issues and slow performance. Pandas on Snowflake eliminates these constraints by running Python code directly in Snowflake, with no rewrites needed. This demo shows how to transform and visualize large data sets using the familiar Pandas API with Snowflake’s distributed compute. Boost your data workflows and maintain security and governance, all while staying within the Pandas ecosystem.

How Retail and Media Leaders Drive Customer Satisfaction and Profits with Data and AI

Nearly nine out of 10 business leaders say their organizations’ data ecosystems are ready to build and deploy AI, according to a recent survey. But 84% of the IT practitioners surveyed spend at least one hour a day fixing data problems. Seventy percent spend one to four hours a day remediating data issues, while 14% spend more than four hours each day.

Providing Better Customer Experiences With The Help Of Cortex AI

Headquartered in Sydney, Domain Group is a leading Australian property marketplace. Its mission is to inspire confidence in life’s property decisions, and its property marketplace tools reach an average audience of 7 million Australians every month. A long-time Snowflake customer, Domain has started to work with Cortex Analyst in order to enable its staff to query its large data sets using natural language.

Strengthen Your Cloud Security With Snowflake's Trust Center

Snowflake’s Trust Center, now in general availability, delivers a unified way to identify, address, and monitor security risks. Discover how scanner packages (Security Essentials, CIS Benchmark, and more) streamline compliance checks, reduce costs, and simplify account security across multiple clouds. See how to configure scans, resolve vulnerabilities, and enforce best practices for user authentication, network policies, and more. Take a proactive approach to safeguarding sensitive data and learn how the Trust Center supports Snowflake’s shared security model.

Why Data Collaboration Projects Fail - and How Yours Can Succeed with a Data Clean Room

As privacy standards continue to evolve, businesses face a dual challenge: to uphold ethical standards for data use while seizing the opportunities offered by data collaboration. Enter data clean rooms: a privacy-enhancing solution that allows organizations to share valuable insights without compromising compliance.* If you're new to data clean rooms, our recent blog post “Data Clean Rooms Explained: What You Need to Know About Privacy-First Collaboration” breaks down the fundamentals.

Delivering The Right Message To The Right Person At The Right Time With Help From the AI Data Cloud

2degrees is a full-service telco, infrastructure owner, and energy retailer connecting people and businesses all around New Zealand. The combined business has approximately 1,600 employees who serve 2 million-plus customers.

Agentic AI in Financial Services and Insurance

Many financial services companies are experimenting with AI through pilot programs, but several challenges remain for adoption. Key concerns include data security, the accuracy of large language models (LLMs) and the rigorous scrutiny from regulators regarding AI’s role in financial decision-making. Current use cases are largely internal, with some customer-facing chatbot solutions addressing noncritical service inquiries.