DEVOPSdigest

Snowflake announced new tools and innovations that accelerate how developers build enterprise-grade pipelines, models, and applications with their data.

Snowflake is furthering its mission of eliminating complexity for customers with new developer tooling and native integrations that speed up development, while empowering them to efficiently ship more advanced products in the AI Data Cloud.

“Thousands of developers around the globe already rely on Snowflake as their go-to development platform. Our latest innovations continue to push the boundaries of what builders can create in the AI Data Cloud, bringing familiar, yet powerful experiences to their enterprise data where it already lives,” said Jeff Hollan, Head of Applications and Developer Platform, Snowflake. “Developers can harness the full breadth of Snowflake’s leading scale, performance, and governance to easily deliver large language model-powered applications that unlock value, ultimately putting AI in the hands of more users.”

By harnessing the combination of Dynamic Tables and Snowpipe Streaming, users can unlock low-latency transformation pipelines to fuel AI and machine learning (ML) model development, all within the AI Data Cloud.

Snowflake is now arming developers with even more ways to accelerate their AI development directly on their data in the AI Data Cloud with Snowflake Notebooks (now public preview) natively integrated with the full breadth of the Snowflake platform including Snowpark ML, Streamlit, and Snowflake Cortex AI. Snowflake Notebooks provides a single, easy-to-use development interface for Python, SQL, and Markdown. Developers can also leverage Snowflake Notebooks to experiment and iterate on their ML pipelines, harness AI-powered editing features, simplify data engineering workflows, and more to unlock increased productivity and collaborative development.

Snowflake is also adding a Snowpark pandas API (now public preview), enabling Python developers to work with the same pandas syntax they know and love for even more advanced AI and pipeline development, while benefiting from Snowflake’s performance, scale, and governance for execution.

Snowflake is further delivering developer simplicity with a truly data-centric approach to DevOps, seamlessly integrating development, operations, and data management within a single platform. By defining the desired state of their data pipelines with infrastructure-as-code principles, rather than scripting complex workflows line by line, Snowflake is prioritizing a declarative approach to development with the new Database Change Management (now public preview) feature. In addition, data engineers and developers can now use Snowflake’s new Git integration (now public preview) to enhance development collaboration across teams and streamline deployments across different environments, leverage Snowflake's Python API (generally available soon) to efficiently manage resources, use the open source Snowflake CLI (generally available soon) as a single interface to manage CI/CD pipelines, and more.

Snowflake is also unveiling Snowflake Trail, a rich set of observability capabilities that provide enhanced visibility into data quality, pipelines, and applications, empowering developers to monitor, troubleshoot, and optimize their workflows with ease. Snowflake is providing built-in telemetry signals for Snowpark and Snowpark Container Services, enabling users to easily diagnose and debug errors using metrics, logs, and distributed tracing — without having to manually set up agents or transfer data. Additionally, Snowflake Trail is built with OpenTelemetry standards so developers can integrate with popular observability and alert platforms including Datadog, Grafana, Metaplane, PagerDuty, Slack, and more, in addition to working natively in Snowsight. Snowflake also partners with observability platforms such as Monte Carlo and Observe to provide end-to-end observability to customers.

Snowflake is also announcing the Snowflake Native App Framework integration with Snowpark Container Services (now public preview on AWS). The integration enables organizations to extend the breadth and variety of applications they build in the AI Data Cloud using configurable GPU and CPU instances to fit a range of use cases spanning computer vision automation, geospatial data analysis, ML applications for enterprises, and more.

Application developers can build their AI-powered Snowflake Native Apps once, and then deploy and distribute them across clouds and regions to thousands of Snowflake customers through Snowflake Marketplace, with over 160 total Snowflake Native Apps3 already available today. Many of the world’s largest organizations rely on Snowflake Marketplace as their distribution platform for unlocking entirely new revenue streams, distributing their Snowflake Native Apps, and accelerating monetization and procurement of those apps. In addition, hundreds of startups are choosing to build their entire businesses on Snowflake, with a handful of providers including Maxa, My Data Outlet, and RelationalAI earning millions from their apps by distributing them on Snowflake Marketplace.