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Improve SEO descriptions (#2819)
Update SEO descriptions with AI and human checks Republish remaining non english guides to load SEO description tag in AEM --------- Co-authored-by: annafil <[email protected]> Co-authored-by: sfc-gh-ridasafdar <[email protected]>
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site/sfguides/src/a-comprehensive-guide-creating-graphql-api-on-top-of-snowflake-using-propel/a-comprehensive-guide-creating-graphql-api-on-top-of-snowflake-using-propel.md

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id: a-comprehensive-guide-creating-graphql-api-on-top-of-snowflake-using-propel
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/product/platform, snowflake-site:taxonomy/snowflake-feature/external-collaboration
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language: en
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summary: This guide teaches how to create a GraphQL API using Propel on Snowflake, focusing on setup, configuration, and development of a high-performance API.
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summary: Create high-performance GraphQL APIs on Snowflake using Propel for real-time analytics, data applications, and developer access.
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environments: web
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status: Published
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feedback link: https://github.com/Snowflake-Labs/sfguides/issues

site/sfguides/src/a-comprehensive-guide-to-ingesting-data-into-snowflake/a-comprehensive-guide-to-ingesting-data-into-snowflake.md

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summary: Learn how to ingest data into Snowflake with Python Connector, Streaming SDK, Snowpipe, and Kafka
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summary: Master data ingestion in Snowflake with Kafka, Snowpipe automation, and streaming for any loading scenario.
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id: a-comprehensive-guide-to-ingesting-data-into-snowflake
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/product/data-engineering, snowflake-site:taxonomy/snowflake-feature/ingestion, snowflake-site:taxonomy/snowflake-feature/connectors
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language: en

site/sfguides/src/a-dataiku-and-snowflake-guide-to-data-science/a-dataiku-and-snowflake-guide-to-data-science.md

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id: a-dataiku-and-snowflake-guide-to-data-science
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/solution-center/certification/partner-solution, snowflake-site:taxonomy/product/ai, snowflake-site:taxonomy/product/data-engineering
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language: en
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summary: This is an introduction to Dataiku and Snowflake
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summary: Build collaborative data science workflows with Dataiku and Snowflake for visual ML pipelines and team analytics.
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environments: web
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status: Published
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feedback link: https://github.com/Snowflake-Labs/sfguides/issues

site/sfguides/src/a-faster-path-to-operational-ai-with-continual-and-snowflake/a-faster-path-to-operational-ai-with-continual-and-snowflake.md

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id: a-faster-path-to-operational-ai-with-continual-and-snowflake
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/product/ai, snowflake-site:taxonomy/snowflake-feature/external-collaboration, snowflake-site:taxonomy/snowflake-feature/ml-functions
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language: en
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summary: Build an operational, continually updating predictive model for customer churn with Snowflake and Continual
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summary: Operationalize AI faster with Continual's AutoML platform and Snowflake for automated prediction pipelines and ML deployment.
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environments: web
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status: Published
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feedback link: https://github.com/Snowflake-Labs/sfguides/issues

site/sfguides/src/a-guide-to-kipi-marketing-mix-modelling-analytics-app/a-guide-to-kipi-marketing-mix-modelling-analytics-app.md

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id: a-guide-to-kipi-marketing-mix-modelling-analytics-app
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/solution-center/certification/partner-solution, snowflake-site:taxonomy/solution-center/includes/architecture, snowflake-site:taxonomy/product/applications-and-collaboration, snowflake-site:taxonomy/snowflake-feature/external-collaboration
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language: en
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summary: This is a sample Snowflake Guide
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summary: Install and explore Kipi's Marketing Mix Modeling Native App to optimize budget allocation, analyze campaigns, and improve marketing ROI in Snowflake.
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environments: web
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status: Published
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feedback link: https://github.com/Snowflake-Labs/sfguides/issues

site/sfguides/src/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku/a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku.md

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id: a-no-code-approach-to-machine-learning-with-snowflake-and-dataiku
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/solution-center/certification/partner-solution, snowflake-site:taxonomy/product/ai
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language: en
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summary: This is an introduction to Dataiku and Snowflake
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summary: Build ML models without code using Dataiku's visual interface connected to Snowflake for rapid experimentation.
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environments: web
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status: Published
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feedback link: https://github.com/Snowflake-Labs/sfguides/issues

site/sfguides/src/a-postman-tutorial-for-snowflake-sql-api/a-postman-tutorial-for-snowflake-sql-api.md

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id: a-postman-tutorial-for-snowflake-sql-api
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/product/platform, snowflake-site:taxonomy/snowflake-feature/external-collaboration
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language: en
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summary: Explore the Snowflake SQL API with Postman
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summary: Test and explore Snowflake SQL API endpoints using Postman for REST-based query execution, automation, and integration.
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environments: web
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status: Published
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feedback link: https://github.com/loopDelicious/sfquickstarts

site/sfguides/src/abt-bestbuy-entity-resolution/abt-bestbuy-entity-resolution.md

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author: Joshua Rauh and Ben Marzec
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id: abt-bestbuy-entity-resolution
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summary: Build an end-to-end entity resolution solution using Snowflake Cortex AI, Streamlit, and hybrid matching techniques to reconcile product data between competing retailers
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summary: Resolve product entities across retail data sources using AI-driven matching for unified product analytics with Snowflake Cortex and Streamlit.
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/product/data-engineering, snowflake-site:taxonomy/snowflake-feature/cortex-llm-functions
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language: en
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environments: web

site/sfguides/src/accelerate-data-transformation-with-the-telecom-data-cloud/accelerate-data-transformation-with-the-telecom-data-cloud.md

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id: accelerate-data-transformation-with-the-telecom-data-cloud
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/solution-center/certification/certified-solution, snowflake-site:taxonomy/solution-center/includes/architecture, snowflake-site:taxonomy/industry/telecom, snowflake-site:taxonomy/product/data-engineering, snowflake-site:taxonomy/snowflake-feature/transformation
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language: en
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summary: This is a guide for getting started with Data Integration using Informatica Data Management Cloud
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summary: Transform telecom data with the Informatica Intelligent Cloud Services Accelerator for Snowflake: network analytics, customer insights, and 5G optimization.
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environments: web
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status: Published
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feedback link: https://github.com/Snowflake-Labs/sfguides/issues

site/sfguides/src/accelerate-topic-modeling-with-gpus-in-snowflake-ml/accelerate-topic-modeling-with-gpus-in-snowflake-ml.md

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author: Chanin Nantasenamat, Vinay Sridhar, Lucy Zhu, Dureti Shemsi
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id: accelerate-topic-modeling-with-gpus-in-snowflake-ml
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summary: This guide covers how to use NVIDIA's cuML and cuDF libraries, now pre-installed in Snowflake ML, to accelerate scikit-learn and pandas workflows with zero code changes.
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summary: Accelerate topic modeling with GPU acceleration. NVIDIA's cuML and cuDF libraries are now in Snowflake ML for fast text analysis, document clustering, and content classification.
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categories: snowflake-site:taxonomy/solution-center/certification/quickstart, snowflake-site:taxonomy/product/data-engineering, snowflake-site:taxonomy/snowflake-feature/snowflake-ml-functions,
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environments: web
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language: en

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