Connect AI to Your Data: Simplify the Entire Development Lifecycle

Connect AI to Your Data: Simplify the Entire Development Lifecycle

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JUN 02, 2026 / 8 min read Product and Technology Simplify the Entire Data Development Lifecycle

Maria Ho +2

By the time most enterprise data reaches the systems that are meant to act on it, it's often stale. That lag is the difference between AI agents making a useful answer or a costly mistake. Agentic AI can only deliver intelligent decisions when it has continuous access to fresh information.

The demand on data engineering teams has shifted toward real-time pipelines and event-driven architectures as more organizations push agentic AI into production — highlighting the need to connect and govern more sources even as they undergo constant change. But what teams are being asked to build has outpaced what their data platform can currently support.

At Summit 2026, Snowflake is strengthening the platform to help data engineering teams succeed in the AI era. This includes notable releases like a native Apache Kafka-compatible streaming service and AI-powered capabilities that reduce data movement and migration costs.

These improvements reduce the time data engineers spend on infrastructure management and manual orchestration, enabling them to spend less time on plumbing and more time on the outcomes that AI makes possible with Snowflake CoCo serving as the common thread that turns complex setup into a guided conversation.

Stream data at the speed AI demands

Agents observe, decide, act, learn and feed that learning into the next decision. Each decision loop should make the next response more accurate, more personal and more actionable. That cycle runs continuously, which means the data feeding it has to flow continuously too. Organizations running Kafka already have the streaming backbone this cycle demands. The problem is that operating it alongside a separate analytics platform means paying for, governing and staffing two separate systems, while the data still arrives late to the place where decisions actually happen.

Datastream (private preview soon) is Snowflake's native, Apache Kafka-compatible streaming service that is designed to collapse that operational overhead into a single governed platform. Data lands continuously as native Snowflake or open Apache Iceberg™ tables, queryable in seconds. Topics are secured with Snowflake role-based access control (RBAC), and tables inherit the full power of Horizon Catalog, including classification, lineage and masking policies. Data is governed the moment it arrives. Simply describe the streaming pipeline you need, and CoCo will handle Datastream's authentication and onboard teams in minutes without needing deep Kafka expertise.

Figure 1: Using CoCo to set up Datastream.

Datastream is purpose-built for organizations that want to replace their Kafka infrastructure with a native Snowflake service. Snowpipe Streaming High-Performance Architecture is a direct ingestion API for teams streaming data from their own applications, including from existing Kafka clusters via the Kafka Connector. Today, Cboe Global Markets, a financial exchange market operator, processes 190 billion rows of market data daily and queries it in under 30 seconds to give traders and analysts real-time visibility into market activity. At Summit, enhancements to Snowpipe Streaming include:

Kafka Connector 4.0 (generally available) offers server-side ingestion up to 10 GB/s per table and reduces client-side resources by up to 30% 1 , so teams scale throughput without sacrificing cost.

Error logging (generally available) captures failed rows in a SQL-queryable table with full context, so teams catch data quality issues before agents act on bad inputs.

With multi-language SDK support (generally available), teams stream from their familiar stack including Java, Python, Node.js and a REST interface.

Elastic Channels (private preview) enables thousands of clients to concurrently stream gigabytes per second to a table through a shared, auto-scaling endpoint, reducing the development time to build and scale streaming pipelines.

Durable Acknowledgments (private preview) removes the window of potential data loss between ingestion and commit. Mission-critical pipelines never feed agents incomplete data.

Pipelines that manage themselves

Getting data into Snowflake in real time is only half the job. The other half is turning that raw stream into something analysts, models and agents can actually consume. That transformation layer needs to run continuously, handle its own retries and refresh logic, and stay reliable without a dedicated engineer constantly watching over it. Teams move faster when pipelines manage themselves.

At Summit, Sergey Labetsik of Wind Creek Hospitality demonstrated how migrating a dbt batch job — previously running on a 30-minute schedule — to a Dynamic Tables pipeline cut end-to-end latency to under a minute, delivering food vouchers to guests the moment they earned them.

And this declarative path has gotten faster and more flexible. Performance enhancements (generally available) offer up to 2.8x faster refresh for common Dynamic Table workloads 2 . Custom incrementalization (public preview) lets engineers use MERGE or INSERT statements for transformations that cannot be expressed declaratively, while retaining full Dynamic Tables automation.

Figure 2: Up to 2.8x faster refresh performance for Dynamic Tables with improvements to how Snowflake works with aggregate functions, qualify rank (SCD-1), cluster-by, joins and more.

DCM Projects (public preview) give teams a single, controlled workflow to declaratively define infrastructure, preview and deploy changes across environments and keep a full audit trail of every deployment. dbt Projects on Snowflake, now even faster with Fusion support (generally available), extends that same philosophy to dbt users.

CoCo skills for Snowpipe Streaming, Dynamic Tables and dbt Projects accelerate setup and troubleshooting across these workflows, letting engineers focus on pipeline logic instead of boilerplate.

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