Systems | Development | Analytics | API | Testing

October 2024

Tools for the Next Era: The Modern Marketing Data Stack 2025

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.

From Creativity to Analytics: Gen AI's Future in Adtech and Martech

Adtech and martech companies are engaged in a fierce battle for audience attention. Customers are bombarded with thousands of ads and marketing messages every day, and the average attention span is plummeting, so it’s no wonder they tune out — or turn on ad blockers. But it’s not all doom and gloom. The global adtech market is expected to grow at a rate of 22.4% through 2030, and martech’s projected growth rate is 18.5% through 2032.

Unlock the Value of Your Sensitive Data with Differential Privacy, Now Generally Available

The Snowflake AI Data Cloud has democratized data for thousands of customers, removing data silos and powering data sharing and collaboration use cases. Many customers have been able to unlock enormous value from their data with Snowflake, including safely collaborating on sensitive data using Snowflake Data Clean Rooms and Data Governance features. However, some highly sensitive data has remained off-limits due to regulatory requirements and privacy concerns — until now.

Leveraging Snowflake And AI To Create Personalized Customer Experiences At Scale

In this episode of the "Data Cloud Podcast", Bill Stratton, Global Head of Media, Entertainment & Advertising at Snowflake, sits down with Ravi Kandikonda, Sr. VP of Marketing at Zillow. Ravi shares his experiences and insights on modern software development, talks about how his academic background prepared him for modern marketing, and what Zillow is doing to approach personalization at scale.

Adobe and Snowflake Deepen Partnership to Rewrite the Next Era of Customer Experience

Adobe launched Adobe Experience Platform Federated Audience Composition, now generally available on Snowflake, allowing organizations to unlock seamless interoperability for marketers by integrating Snowflake's AI Data Cloud with Adobe Real-Time Customer Data Platform (CDP) and Adobe Journey Optimizer.

Govern an Open Lakehouse with Snowflake Open Catalog, a Managed Service for Apache Polaris

To enhance security and ease operational burden, many organizations with data lakes or lakehouses want flexibility to securely integrate their tools of choice on a single copy of data. An open standard for storage format and catalog API has helped, but there’s still a need for open standards for the catalog, including a consistent way to apply security access controls to data.

Introducing Container Runtime: Enabling Flexible, Scalable Training and Inference on GPUs from a Snowflake Notebook

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.

Accelerate End-to-End RAG Development in Snowflake with New SQL Functions for Document Preprocessing

As organizations increasingly seek to enhance decision-making and drive operational efficiencies by making knowledge in documents accessible via conversational applications, a RAG-based application framework has quickly become the most efficient and scalable approach. As RAG-based application development continues to grow, the solutions to process and manage the documents that power these applications need to evolve with scalability and efficiency in mind. Until now, document preparation (e.g.

How Solid Data Strategies are Fueling Generative AI Innovation

If innovation is the ultimate goal in business and technology today, then consider generative AI (gen AI) the vehicle taking us there — and a strong data strategy, the fuel. Despite all its promise of productivity gains and new discoveries, gen AI alone can't do it all. The technology needs a "very ready" data foundation to feed on, something the vast majority of businesses today (78%) do not possess, according to a new report by MIT Technology Review Insights, in partnership with Snowflake.

Protecting Customer Data In A Collaborative, Data-Sharing World | OneTrust

In this episode of "Powered by Snowflake," we hear from Ojas Rege, GM of Privacy and Data Governance at OneTrust, about his company's consent and privacy platform, a platform that helps organizations manage customer data in a way that allows for data sharing and collaboration without compromising data security and customer privacy. The conversation includes a discussion about the role Snowflake technology plays in providing this kind of capability as well as an in-depth demo of the OneTrust Snowflake Native Application.

Closing the Healthcare Gap: A Conversation With Rajesh Viswanathan

In this episode of the "Data Cloud Podcast," Todd Crosslin, Global Industry Principal Healthcare and Life Sciences at Snowflake, sits down with Rajesh Viswanathan, CTO at Inovalon. They discuss Rajesh's journey into healthcare, the complexities of the U.S. healthcare system, the role of data and AI in transforming healthcare, and Inovalon's strategies to improve patient outcomes and healthcare economics.

Build and Manage ML Features for Production-Grade Pipelines with Snowflake Feature Store

When scaling data science and ML workloads, organizations frequently encounter challenges in building large, robust production ML pipelines. Common issues include redundant efforts between development and production teams, as well as inconsistencies between the features used in training and those in the serving stack, which can lead to decreased performance. Many teams turn to feature stores to create a centralized repository that maintains a consistent and up-to-date set of ML features.

Driving Innovation and Efficiency with Gen AI in Life Sciences

AI has profoundly impacted the life sciences industry for the past couple of decades. In the 2000s, researchers were able to use AI to analyze the human genome, identifying genetic markers and variations that could predict an individual’s susceptibility to certain diseases. This opened the door to personalized medicine and more effective therapies for genetic disorders.