Experience AI-Powered Analytics and Migrations at Warp Speed with Snowflake’s Latest Innovations

Today's complex data landscape holds vast, untapped information in unstructured data formats such as text, images and audio. Enterprises know they must leverage this data alongside structured assets for informed decisions, but the challenge lies in unlocking its potential. The demand is clear: Analytics solutions must be easy to use, inherently intelligent, and fast in delivering insights — on all data. Yet many enterprises grapple with the limitations of legacy systems, and the often-arduous journey of a data warehouse migration can significantly hinder progress.

That’s why at Snowflake, we're not just keeping pace with these challenges; we're architecting for the future of AI-powered analytics. Today, we are thrilled to announce a suite of advancements to help supercharge data analysts and architects with AI. By enabling seamless AI-powered migrations from legacy environments to a modern AI-powered analytics engine, we empower customers to future-proof their broader data platform strategy. This foundation unlocks the power of AI-driven insights across all data, transforming analysts into AI engineers. Finally, Snowflake provides world-class performance across all workloads, including peak Iceberg performance as the leading lakehouse analytics engine, consistently surpassing the competition. All these announcements will help you transform raw data into actionable insights with remarkable ease and speed powered by AI.

Go from legacy to leading edge: End-to-end data ecosystem migrations — powered by AI

At Snowflake, we believe a modern AI and data platform is easy, connected and trusted. This is achieved through capabilities such as intelligent and automated cluster management, built-in governance, zero-ETL collaboration and continuous performance optimization, which reduce onboarding and manual platform administration, thus saving time and money. Consequently, migrating legacy data warehouses, business intelligence (BI) systems and Spark-based data lakes or lakehouses to the Snowflake AI Data Cloud is key to unlocking innovation, efficiency and growth across an entire organization.

The journey from legacy systems to the Snowflake AI Data Cloud has never been more accessible or cost effective, thanks to SnowConvert AI. With data ecosystem migration agents powered by Snowflake Cortex AI, SnowConvert AI is your free automated solution designed to dramatically reduce the complexities, costs and timelines associated with data warehouse, BI and ETL migrations. It intelligently analyzes your existing code, automating code conversion and data validation while streamlining the entire migration process.

For data warehouse migrations, SnowConvert AI provides robust support for a growing list of source platforms. We're excited to announce expanded support, all generally available:

• Greenplum (tables and views): Accelerate your schema migration with code conversion for Greenplum tables and views.

• Netezza (tables and views): Accelerate your schema migration with code conversion for Netezza tables and views.

• Postgres (tables and views): Accelerate your schema migration with code conversion for Postgres tables and views.

• BigQuery (tables and views): Simplify the migration of complex logic with automated conversion of BigQuery tables and views.

• Sybase (tables and views): Seamlessly migrate critical business rules with automated conversion of Sybase tables and views.

• Microsoft Synapse: Benefit from enhanced automation for migrating your Synapse data warehouse workloads, with code conversion support for tables, views, stored procedures and more.

• Existing support: Continue to leverage SnowConvert AI's proven capabilities for platforms such as Teradata, Oracle, SQL Server and Amazon Redshift.

We are also continuously innovating to make your migration to Snowflake as smooth and efficient as possible. Here is a look at the latest advancements, aligning with key phases of your migration:

End-to-end data migrations (generally available soon): For specific platforms such as Amazon Redshift and SQL Server, enjoy a unified, end-to-end experience within SnowConvert AI. This provides a single view to extract, convert, migrate, validate and deploy both code and data, simplifying the process and accelerating timelines.

• AI-powered Migration Assistant (generally available): SnowConvert AI begins by deeply understanding the grammar of your legacy database to automatically convert the majority of your code to Snowflake. For any remaining code, it provides AI-driven explanations and suggested fixes for errors, warnings and issues directly within the tool, streamlining development and accelerating the conversion of complex database objects such as user-defined functions and stored procedures. 

• AI-powered code verification (in private preview): Automatically verify converted code with data ecosystem migration agents, powered by Snowflake Cortex AI.This significantly speeds up migrations by testing converted objects with synthetic data on both the source system and Snowflake, enabling early issue detection. This proactive approach — identifying issues during code conversion rather than in User Acceptance Testing (UAT) — ensures a considerably smoother transition process.

• AI-powered data validation (in public preview soon): Ensure the integrity of your migrated data in Snowflake with intelligent and efficient comparisons between source and target data. This capability effortlessly matches types, semantics and values, reducing manual effort, detecting issues early and enhancing overall reliability.

• Ecosystem migrations: Seamlessly integrate your existing tools with Snowflake:

  • SSIS repointing (in public preview): Automatically update your SQL Server Integration Services (SSIS) packages to point to Snowflake, simplifying your ETL migration.

  • Power BI repointing (in public preview soon): Automatically update your Power BI report connections and code to seamlessly connect to Snowflake, preserving your BI investments with minimal disruption.

• Get started on the Snowflake Migration Hub (generally available): Navigate the migration process with ease using the centralized Snowflake Migration Hub within Snowsight. Access all migration-related solutions and information, including SnowConvert AI and the Snowpark Migration Accelerator, in one intuitive location.

By leveraging the power of SnowConvert AI and the comprehensive capabilities of the Snowflake AI Data Cloud, your journey from legacy systems to a leading-edge AI-powered data ecosystem is now faster, more cost-effective and more seamless than ever before.

Imagine a world where analyzing all your data using AI, regardless of its structure, is as straightforward as writing SQL. With Snowflake Cortex AISQL (in public preview), this vision becomes a reality. Snowflake is simplifying complex AI pipelines, empowering data analysts to transcend traditional boundaries and become true AI engineers. The beauty of Cortex AISQL lies in its ease of use — its intuitive syntax allows you to seamlessly combine and analyze structured and unstructured data within a single table using familiar SQL. This accessibility democratizes AI analytics, making its power available to any analyst across your enterprise, without the need for specialized AI expertise or complex setups.

Forget the complexities of integrating separate data services and infrastructure. Cortex AISQL operates natively within Snowflake, allowing you to directly analyze your multi-modal data without the overhead of external integrations. Experience optimized performance as you transform unstructured data into queryable insights across massive data sets, all within Snowflake’s powerful and cost-efficient engine. By eliminating the need for data egress to external services, we streamline workflows and accelerate the extraction of critical insights from all your enterprise data. This empowers your analysts, unlocking powerful AI capabilities without requiring them to learn new skills or rely on dedicated AI engineering resources.

Complementing Cortex AI, Snowflake Semantic Views (public preview) offer a powerful way to bridge the gap between raw data and business understanding. By allowing you to define and store business metrics and entity relationships directly within Snowflake, these views provide a consistent and unified layer whether you’re using Snowflake Cortex Analyst, Snowflake Intelligence, BI tools or even direct SQL queries, guaranteeing accurate and performant results across diverse user interfaces — including partner integrations with Hex, Omni and Sigma. 

We're also enhancing existing capabilities with QAS (generally available) so more queries are now automatically accelerated. To further simplify working with time-based data, we’ve expanded our suite of native functions for time series analytics to include powerful functions for upsampling data with the RESAMPLE clause and new functions like INTERPOLATE_LINEAR to help fill data gaps. Working with temporal data often brings unique challenges — especially when data points are missing due to expected gaps such as market holidays, or unexpected ones such as brief sensor outages. In the past, handling such irregularities required customers to write complex and cumbersome logic to reshape and fill their data into a consistent, analytics-ready format. Now, this can be done easily and efficiently using the new suite of time series functions. What once took many lines of brittle SQL can now be expressed with clarity — making your temporal analysis both easy and performant.

Embrace new dimensions of data type support

To accommodate increasingly large and complex data sets, we've significantly increased the size limits for storing data in both native Snowflake tables and Iceberg by 8x (generally available). Furthermore, the introduction of Data Types - Structured Type for Snowflake native tables (generally available), allows you to define and enforce precise schemas for complex data structures within your FDN, ensuring enhanced data governance. With Data Types - XML Support (generally available), you can now natively ingest, parse and query XML data, transforming it into a readily analyzable semi-structured format. 

Experience more than 2x faster analytics performance 

At Snowflake, we have been innovating to make analytics even faster and even easier to use with better price/performance. 

Since Snowflake’s founding in 2012, we strive to be the market leader in fast performance for all workloads and continue to iterate rapidly to automatically improve by doing the heavy lifting on behalf of our customers. We are excited to announce Standard Warehouse - Generation 2 (Gen2) (generally available), an updated version of Snowflake’s current Standard Warehouse that has upgraded hardware and additional performance enhancements such as those that enhance Delete, Update and Merge operations and speed up table scan operations. Over the past 12 months ending May 2, 2025, Snowflake has delivered 2.1x faster performance for core analytics workloads on Snowflake tables through Gen2. 

Compared to Managed Spark, this powerful new warehouse from Snowflake delivers 1.9x faster performance. 

In addition, Gen2 also provides 2.3x higher throughput for Concurrent BI workloads and up to 4.4x faster Delete, Update and Merge operations compared to Standard Warehouses.

Snowflake is also excited to introduce Snowflake Adaptive Compute (in private preview), the next evolution of our compute service that takes away the undifferentiated heavy lifting of making infrastructure choices to save time and money. Adaptive Compute enables ultimate ease of use by automatically selecting the appropriate cluster size(s), the number of clusters, and auto-suspend/resume duration for jobs on your behalf to minimize required configurations. Query routing is even done intelligently to the right-sized clusters without any user action. Furthermore, Adaptive Compute provides better price/performance not only from leveraging the latest and greatest hardware and performance enhancements from Snowflake but also from queries optimally sharing a pool of compute resources across your account to maximize efficiency. Warehouses created using the Adaptive Compute service are called Adaptive Warehouses, which are in private preview.  

To provide greater visibility into your workload’s performance, Performance Explorer (generally available soon) offers deep, interactive insights within Snowsight, empowering administrators to quickly identify and address any bottlenecks. And for all users, Query Insights (in private preview) provides valuable information to understand the key factors influencing query performance, enabling proactive optimization and cost reduction.

Get the leading lakehouse analytics engine for peak Iceberg performance

For organizations embracing open data lakehouse architectures, we are excited to announce we are extending Snowflake's fast, efficient core engine to your Apache Iceberg tables with Merge on Read (in public preview), allowing Snowflake to read from any Iceberg table. In addition, we are launching a plethora of performance improvements for Iceberg tables to help you access analytics at scale, including: 

• Search optimization for Iceberg (generally available): Speeds up selective queries on Snowflake-managed Iceberg tables by reducing how much data Snowflake needs to scan with a fully managed, optimized, intelligent search optimization service. 

• Query acceleration service for Iceberg (generally available): Automatically speeds up compute-intensive queries without requiring any changes to SQL or the data mode for Snowflake-managed Iceberg tables.

• Iceberg compaction support (in private preview): Allows Snowflake to automatically maintain a defined target file size for greater control on the cost and performance of reading and writing to Apache Iceberg files.

• Pruning for geospatial data types in Iceberg (in private preview): Applies pruning techniques to speed up geospatial lookup queries, specifically for GEOMETRY data types.

• Adaptive I/O and memory tuning (generally available): Dynamically adjusts system resources during query execution based on real-time metrics to optimize performance and reduce network overhead.

• Pruning optimizations (generally available): Improves efficiency for selective queries with optimized bloom filters to reduce the amount of data scanned.

• Join performance improvements (generally available): Enhances the speed and efficiency of join operations, especially for complex keys, leading to faster query results.

• Improved operator efficiency (generally available): Improves query performance for complex queries by optimizing data distribution between query operators adaptively during query execution.

All these Iceberg improvements — combined with the hardware upgrades and additional performance enhancements we’ve made through Gen2 — have resulted in 2.4x* faster core analytics performance for externally managed Iceberg tables and 2.1x** faster core analytics performance for Snowflake-managed Iceberg tables. And because Snowflake has a consumption-based pricing model, these faster processing times result directly in reduced costs. 

Learn more

These innovations in our analytics product category represent a significant leap forward in empowering organizations to unlock the full potential of their data. From gaining unparalleled AI advantages across diverse data types to simplifying complex migrations and achieving exceptional performance, Snowflake is your partner in building the future of analytics. We're excited about the possibilities these new capabilities unlock for easy, intelligent and fast analytics and look forward to seeing the impact they will have on your data journey. 

To learn more, visit the Snowflake for Analytics web page. 

Forward-looking statements

This article contains forward-looking statements, including about our future product offerings, which are not commitments to deliver any product offerings. Actual results and offerings may differ and are subject to known and unknown risk and uncertainties. See our latest 10-Q for more information.