Latest news with #AISQL
Business Wire
4 days ago
- Business
- Business Wire
Sigma Launches Native Semantic Layer Integration and AI SQL Capabilities on Snowflake AI Data Cloud
SAN FRANCISCO--(BUSINESS WIRE)--Sigma, the industry-leading analytics platform with unique cloud data platform writeback capabilities, today announced at Snowflake Summit 2025, two major platform innovations in partnership with Snowflake: a first-class integration with Snowflake Semantic Views and support for AI SQL, Snowflake's breakthrough feature for querying unstructured data. Together, these advances enable governed semantic exploration and file-based AI-powered analysis—directly in Sigma's intuitive, spreadsheet-like interface. The combined innovations mark a leap toward unified analytics where both structured metrics and raw human context—contracts, images, PDFs, and text—are queryable side-by-side in a single governed system. 'Sigma is building toward a future where every layer of the data stack speaks the same language—defined once, executed everywhere.' Query Semantic Views Directly in Sigma With this integration, Sigma unlocks warehouse-defined metrics, dimensions, and relationships for downstream analysis, dashboards, and apps – cementing the data warehouse as the single source of truth for semantics. This new integration offers joint customers the most seamless, warehouse-native analytics experience on the market. By partnering with Snowflake, the AI Data Cloud company, Sigma is helping to fully realize a long-held industry vision: semantic logic defined once, governed centrally, and accessed directly in the warehouse—no duplication, no drift. Together, the companies are mobilizing the world's data to help organizations operate in an environment where semantic logic lives natively in the warehouse, not duplicated across disconnected tools. 'Sigma's integration with Snowflake Semantic Views isn't just compatible — it's truly native, built for flexibility, scale, and the next generation of analytics,' said Mike Palmer, CEO of Sigma. 'By meeting the semantic layer where it belongs, we're giving business teams instant access to governed metrics and logic without compromise. And this is just the beginning. From bi-directional syncs to visual semantic exploration, Sigma is building toward a unified modeling experience that brings clarity and control to every layer of the data stack.' 'The integration between Sigma and Snowflake's Semantic Views marks an important step forward in enabling enterprises to leverage the state-of-the-art AI solutions available with Snowflake Intelligence and Cortex Analyst,' said Carl Perry, Head of Analytics, Snowflake. 'This advancement helps our customers maximize the value of their data within Snowflake's AI Data Cloud through AI and BI experiences, creating more efficient and powerful workflows for their teams.' 'This is a major leap forward in delivering a consistent, governed experience powered entirely by Snowflake,' added Palmer. 'Sigma is building toward a future where every layer of the data stack speaks the same language—defined once, executed everywhere.' Bringing Structure to Unstructured Data - Powered by Cortex AISQL Also announced today at Snowflake Summit 2025, is the news that Sigma is among the first analytics platforms to fully support Snowflake AI SQL, a new capability that lets users query unstructured data—like contracts, receipts, product specs, and image files—as if it lived in a table. This news comes on the heels of Sigma's recent launch of its new File Column Type feature, allowing end users to connect unstructured content with structured data for the first time, making complex, real-world workflows fully executable inside Sigma. Teams can upload files with Sigma, run them through Snowflake's powerful LLM-based functions, and analyze the structured results alongside traditional datasets—no pipelines and no special tools required. 'For decades, legacy BI tools assumed your data was clean, structured, and waiting politely in rows and columns,' said Palmer. 'But some of the most important business decisions are made with the messy stuff: legal documents, compliance PDFs, screenshots, receipts, product specs, and annotated images. Historically, those formats required a human in the loop: to read, interpret, and manually extract insights. That's the bottleneck AI SQL removes. Sigma and Snowflake turn human knowledge into scalable systems, unlocking entirely new types of analysis across industries and teams.' 'Every organization recognizes the potential of AI. But too often, harnessing AI means overcoming complex infrastructure, performance limitations, high costs, and a reliance on engineers to build custom pipelines,' said Perry. 'We're removing those barriers, whether it's enabling anyone to analyze and act on all their data with Cortex AISQL or accelerating migrations off legacy systems through SnowConvert AI. By empowering teams to move faster, work smarter, and turn data into real impact, we're reimagining analytics for the AI era.' Snowflake's AI SQL functions analyze the content using LLMs, and Sigma picks up the structured output and renders it live in dashboards or workflows. This unlocks transformative use cases: Process thousands of vendor contracts Review receipts as part of claims workflows Extract key clauses from dense legal agreements Attach evidence to operational data for full-context analytics There's no need for custom pipelines, reformatting, or manual review. Just files in, answers out. Governed, traceable, and ready to use. Joint customers can start using the semantic layer integration immediately through their existing Snowflake and Sigma environments as well as the full support for Cortex AISQL. For more information on Sigma's integration with Snowflake Semantic Views, click here and for more information on Snowflake's AI SQL function, read here. ABOUT SIGMA Sigma is business intelligence built for the cloud. With a spreadsheet UI, business users can work in the formulas and functions they already know, while more technical users can write SQL and apply AI models to data. Sigma queries the cloud warehouse directly, making it incredibly fast and secure—data never leaves the warehouse, and Sigma can analyze billions of rows in seconds. Beyond dashboards and reports, teams use Sigma to build custom data apps, which integrate live data with end user input. Sigma is the first analytics platform to enable data writeback, and continues to lead the market with innovation across AI, reporting, and embedded analytics.
Techday NZ
5 days ago
- Business
- Techday NZ
Snowflake unveils AI tools for data analytics, migration
Snowflake has revealed new artificial intelligence (AI) products aimed at simplifying data analytics and accelerating migration from legacy systems for organisations across Canada and globally. The company announced its expansion of enterprise-grade AI with the introduction of Cortex AISQL and SnowConvert AI, designed to enable customers to extract insights from diverse data types while reducing operational costs and complexity. Cortex AISQL incorporates generative AI directly into database queries, allowing teams to analyse and act on multiple kinds of data—ranging from structured numbers to text, images and audio—while using the SQL syntax familiar to data professionals. The solution is intended to bring what Snowflake describes as "industry-leading performance and up to 60% cost savings when filtering or joining data." Organisations such as Hex, Sigma, and TS Imagine are among those already leveraging the capabilities of Cortex AISQL. SnowConvert AI addresses a common challenge faced by enterprises: moving data from existing warehouse and analytics platforms to modern systems. The tool uses AI automation to ease migrations from providers such as Oracle, Teradata, and Google BigQuery, reducing the need for manual re-coding and lowering the risks typically associated with large-scale data projects. "Every organization recognizes the potential of AI. But too often, harnessing AI means overcoming complex infrastructure, performance limitations, high costs, and a reliance on engineers to build custom pipelines. We're removing those barriers, whether it's enabling anyone to analyze and act on all their data with Cortex AISQL or accelerating migrations off legacy systems through SnowConvert AI. By empowering teams to move faster, work smarter, and turn data into real impact, we're reimagining analytics for the AI era," Carl Perry, Head of Analytics at Snowflake, commented on the new developments. "In capital markets, speed and precision are everything. For years, SQL has been the gold standard for transforming data — and now, with Cortex AISQL, we're extending that power to unstructured text. With AISQL, our teams can analyze documents, extract insights, and build intelligence directly in the language they already know — all without complex engineering workflows. It's a game-changer for how fast we can respond to markets and deliver value to clients, while leveraging the Snowflake architecture for high performing SQL processing," Thomas Bodenski, Chief Operating Officer of TS Imagine, said, sharing his perspective as a customer. Snowflake's Cortex AISQL harnesses generative AI—powered by models from providers such as Anthropic, Meta, Mistral, and OpenAI—to introduce advanced query functions into standard SQL. The result is that data analysts can use AI-powered functionalities within the security perimeter of the existing data cloud, without requiring specialist coding or external tools. According to Snowflake, ongoing performance optimisations have demonstrated between 30% and 70% improvements depending on the dataset, and up to 60% cost savings for certain operations. The integration enables organisations to break down traditional data silos. For example, analysts can merge structured customer data with unstructured data like chat transcripts, images, or social media content, and perform tasks including image classification, call transcript analysis and anomaly detection entirely through SQL queries. SnowConvert AI, meanwhile, is designed to make IT infrastructure upgrades more efficient, automating code conversion, report migration and data validation to streamline the transition to new platforms. By accelerating the code conversion and testing phases by two to three times, the tool is aimed at reducing the overall timeline and resource demands associated with digital transformation. Alongside these tools, Snowflake also announced updates to its platform to further support analytics on open source data formats such as Apache Iceberg tables, and launched Standard Warehouse - Generation 2, which introduces hardware and software improvements to boost analytics performance by 2.1 times over previous editions. With the introduction of these AI-powered features, Snowflake is positioning its data cloud as a central platform for enterprises seeking to modernise analytics and data handling capabilities, and to extract actionable business insights from both structured and unstructured sources.
