Snowflake’s Data Cloud is powered by a single engine. From day 1, we have been focusing on consistently evolving and improving this engine to allow existing workloads to run more efficiently and enable new workloads to run on Snowflake. The single engine approach translates into a single experience—from one consistent pricing model to an integrated approach combining performance, security, governance, and the foundation to seamlessly enable cross-region or cross-cloud scenarios.

Going into Snowflake Summit 2022, I was excited to spend time with our customers and partners, and excited to be able to share some of the innovations we’ve been working on. And I was not disappointed! It felt great to experience the energy that only an in-person event can deliver. I relished talking to customers about how our products can help them meet and even surpass their business goals.

Snowflake has once again transformed data management and data analytics with our newest workload—Unistore. For decades, transactional and analytical data have remained separate, significantly limiting how fast organizations could evolve their businesses. With Unistore, organizations can use a single, unified data set to develop and deploy applications, and analyze both transactional and analytical data together in near-real time.

Cybersecurity is a data problem at its core. Yet, security teams haven’t achieved tremendous success in utilizing the modern data stack that data analytics teams have enjoyed for years. Security teams face constant pressure from vulnerabilities and breaches in their infrastructure and supply chains because they remain on a proverbial island with antiquated technology. Cybersecurity leaders must uplevel their strategies by implementing a modern security data lake.

How we eat, exercise, work, and rest play an important role in influencing our health outcomes. It’s been established that healthcare and life sciences (HCLS) organizations can improve health outcomes when they have access to this type of data on patients to inform real-world evidence.

Machine learning (ML), more than any other workflow, has imposed the most stress on modern data architectures. Its success is often contingent on the collaboration of polyglot data teams stitching together SQL- and Python-based pipelines to execute the many steps that take place from data ingestion to ML model inference.

Customer advocacy is one of the Snowflake Support team’s most important roles. Working closely with customers around the world every day, we listen and learn to gain meaningful insights into Snowflake products, the ways our customers use those products, and the challenges they face. We feel a deep responsibility to take those insights and trends, analyze them, and drive positive change on behalf of Snowflake customers.

The skyrocketing value of data has created a global supply and demand for data, data applications, and data services. This new data economy is powered by technologies that enable data access and sharing, including cloud platforms, exchanges, and marketplaces.

Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.

In March, Snowflake continued to enhance its capabilities around data programmability and data pipeline development, with the Snowpark API and stored procedures for Java now in public preview, schema detection now generally available, and the Snowflake SQL API generally available. In addition, Snowflake’s user interface, Snowsight, is generally available. Not to mention an expanded selection of new partners to choose from in Snowflake Data Marketplace.