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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-061a284019","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"isDeveloperGuidesPage":false,"quickstartHeroBreadcrumbs":[{"title":"Unlocking Hidden Value: The Modern Data Architecture for AI-Powered Insurance Claims 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data using Snowpark Connect for Apache Spark, and delivers AI-powered insights through Cortex Analyst and Snowflake CoWork. It provides an end-to-end blueprint for combining open table formats, feature engineering, and natural language analytics to accelerate fraud detection and claims processing.\u003C/p\u003E\n","\u003Ch2\u003EPrerequisites\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with ACCOUNTADMIN role\u003C/li\u003E\u003Cli\u003EAWS account with permissions to create and manage S3 buckets, IAM roles/policies, AWS Glue Data Catalog, and Lake Formation\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo CLI\u003C/a\u003E (CoCo) installed and configured with a valid connection to the Snowflake account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EWhat You'll Learn\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Horizon Catalog\u003C/strong\u003E — How the unified catalog now enables discovery and governance of both external Apache Iceberg tables and Snowflake Native tables from a single pane of glass, without moving data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpark Connect for Apache Spark\u003C/strong\u003E — How to use familiar PySpark APIs to join, enrich, and score data across external Iceberg tables and internal Native tables in a single Spark job running on Snowflake compute\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E — How to publish a Cortex Agent that lets business users ask natural language questions on the aggregated, fraud-scored claims data without writing SQL\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoCo (CoCo)\u003C/strong\u003E — How to use CoCo and interactively deploy the entire solution end-to-end, from creating Snowflake objects and running Spark jobs to setting up Cortex Analyst and Snowflake CoWork\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESolution Architecture\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Modern_Data_Architecture_for_AI-Powered_Insurance_Claims_Insights.jpeg?v=a3cd9659\" alt=\"Solution Architecture\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EArchitecture Overview\u003C/h2\u003E\n","\u003Cp\u003EThis solution leverages AWS and Snowflake services to create an end-to-end data pipeline for insurance claims analytics.\u003C/p\u003E\n","\u003Ch3\u003EComponents\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAWS Account (US-West-2)\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAmazon S3\u003C/strong\u003E - Raw data storage in Apache Iceberg format\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAWS Glue Data Catalog\u003C/strong\u003E - Metadata management for Iceberg tables\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake (US-West-2)\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECatalog Linked Database\u003C/strong\u003E - Connects to AWS Glue Catalog for accessing external Iceberg data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Native Tables\u003C/strong\u003E - Persistent storage for transformed data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Horizon Catalog\u003C/strong\u003E - Central governance layer\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpark Connect for Apache Spark\u003C/strong\u003E - Data transformation and feature engineering\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E - AI-powered agent interactions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E - Natural language to SQL generation\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPhase 1: Data Ingestion & External Cataloging (AWS to Snowflake)\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStep\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EClaims Scenario\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClaims Data Landing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAmazon S3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERaw claims data (e.g., FNOL reports, adjuster notes, images, etc.) is stored in the data lake using Apache Iceberg table format\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetadata Registration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAWS Glue Data Catalog\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe Glue Catalog is updated with Iceberg table metadata, acting as the "source of truth" pointer to the Iceberg data files in S3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECatalog Synchronization\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECatalog Linked Database\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake's Catalog Linked Database connects to AWS Glue Catalog, ingesting the Iceberg metadata and creating a schema and table that points directly to the external Iceberg data in S3\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EPhase 2: Data Transformation & Feature Engineering\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStep\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EClaims Scenario\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Processing & Feature Creation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect for Apache Spark\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe Spark job reads claims data from the Catalog Linked Database, performs ML-based feature engineering (e.g., NLP for claims description, embeddings, calculating loss ratios, geo-spatial analysis, etc.) and writes the enriched features to a Snowflake Native Table using the Iceberg REST API\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Persistence\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpark Connect for Apache Spark (Write-back)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe resulting enriched and curated data is written back to a new Snowflake Native Table. This table is optimized for querying and AI/ML workloads\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EPhase 3: Insight Generation & Agent Interaction\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStep\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EClaims Scenario\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgent Query & Query Orchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake CoWork, Cortex Agents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA claims agent, without writing SQL, asks a natural language question (e.g., "Show me the top 5 high-risk claims filed this week"). Cortex Analyst receives the request, understands the context, and generates the appropriate SQL query to Cortex Analyst\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESQL Generation & Execution\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe Cortex Analyst component receives a user question, translates it into an optimized Snowflake SQL query, and executes it against the Snowflake Native Tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInsight Delivery\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake CoWork\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe end-to-end flow delivers instant answers to complex analytical questions, allowing users to act quickly by converting them to summary alerts or remediation actions\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ECross-Cutting Layer: Governance\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ESnowflake Horizon Catalog\u003C/strong\u003E acts as the central governance layer throughout this entire process, ensuring every user and component (from Snowpark job to Snowflake CoWork) only accesses data according to defined security policies.\u003C/p\u003E\n","\u003Ch2\u003ESolution Deployment\u003C/h2\u003E\n","\u003Ch3\u003EStep 1: Configure AWS Infrastructure (S3, Glue, IAM, Lake Formation)\u003C/h3\u003E\n","\u003Ch4\u003ES3 Bucket for Iceberg Data Files\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS Console → S3 → Create bucket\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EEnter the bucket name: \u003Ccode\u003E<account-id>-us-west-2-insurance-claims-iceberg-data\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect the AWS Region: \u003Ccode\u003Eus-west-2\u003C/code\u003E (must match your Glue catalog region)\u003C/li\u003E\u003Cli\u003ELeave \u003Cstrong\u003EBlock all public access\u003C/strong\u003E enabled (default)\u003C/li\u003E\u003Cli\u003EEnable \u003Cstrong\u003EBucket Versioning\u003C/strong\u003E (recommended for Iceberg table consistency)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate bucket\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EOnce created, create the following folder structure inside the bucket:\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Efnol_reports/\u003C/code\u003E — First Notice of Loss reports\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eadjuster_notes/\u003C/code\u003E — Adjuster notes and assessments\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Einitial_estimates/\u003C/code\u003E — Initial claim estimates\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe folder structure should look as below once the S3 Bucket and Prefixes are created\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/s3-bucket-prefixes.png?v=a3cd9659\" alt=\"S3 Bucket with Prefixes\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EAWS Glue Data Catalog\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EVerify that the Glue Data Catalog is enabled in your region (enabled by default in most regions)\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS Console → Glue → Data Catalog → Databases\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ECreate a Glue Database\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS Console → Glue → Databases → Add database\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EDatabase name: \u003Ccode\u003Einsurance_claims_iceberg_glue_db\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ELocation: \u003Ccode\u003Es3://<account-id>-us-west-2-insurance-claims-iceberg-data/\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate database\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/create-glue-database.png?v=a3cd9659\" alt=\"Glue Database\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EIAM Role with Glue/Lake Formation Permissions\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate an IAM Policy\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS Console → IAM → Policies → Create policy\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EJSON\u003C/strong\u003E tab and paste the following policy. Update the \u003Ccode\u003Eaccount-id\u003C/code\u003E placeholder before saving the policy.\u003C/li\u003E\u003Cli\u003EPolicy name: \u003Ccode\u003Einsurance-claims-iceberg-data-policy\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "Version": "2012-10-17",\n    "Statement": [\n        {\n            "Sid": "GlueAndS3ScopedAccess",\n            "Effect": "Allow",\n            "Action": [\n                "glue:CreateTable",\n                "glue:DeleteDatabase",\n                "glue:GetTables",\n                "glue:GetPartitions",\n                "glue:UpdateTable",\n                "glue:DeleteTable",\n                "glue:GetDatabases",\n                "glue:GetTable",\n                "glue:GetDatabase",\n                "glue:GetPartition",\n                "glue:GetCatalogs",\n                "glue:CreateDatabase",\n                "glue:GetCatalog",\n                "s3:ListBucket",\n                "s3:GetBucketLocation"\n            ],\n            "Resource": [\n                "arn:aws:glue:*:*:table/*/*",\n                "arn:aws:glue:*:*:catalog",\n                "arn:aws:glue:*:*:database/insurance_claims_iceberg_glue_db",\n                "arn:aws:s3:::<account-id>-us-west-2-insurance-claims-iceberg-data"\n            ]\n        },\n        {\n            "Sid": "GlueAndS3GlobalAccess",\n            "Effect": "Allow",\n            "Action": [\n                "glue:GetTables",\n                "glue:GetCatalog",\n                "glue:GetTable",\n                "s3:ListAllMyBuckets",\n                "s3:AbortMultipartUpload",\n                "s3:ListMultipartUploadParts",\n                "s3:GetBucketCors",\n                "s3:GetBucketVersioning",\n                "s3:GetBucketAcl",\n                "s3:GetBucketNotification",\n                "s3:GetBucketPolicy",\n                "lakeformation:GetDataAccess"\n            ],\n            "Resource": "*"\n        },\n        {\n            "Sid": "S3DataAccess",\n            "Effect": "Allow",\n            "Action": [\n                "s3:GetObject",\n                "s3:PutObject",\n                "s3:DeleteObject",\n                "s3:ListBucket",\n                "s3:GetBucketLocation",\n                "s3:ListAllMyBuckets"\n            ],\n            "Resource": [\n                "arn:aws:s3:::<account-id>-us-west-2-insurance-claims-iceberg-data",\n                "arn:aws:s3:::<account-id>-us-west-2-insurance-claims-iceberg-data/*"\n            ]\n        }\n    ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate an IAM Role\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS Console → IAM → Roles → Create role\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ETrusted entity type: \u003Cstrong\u003EAWS account\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EAnother AWS account\u003C/strong\u003E (enter a placeholder account ID for now; this will be updated after the Snowflake catalog integration is created)\u003C/li\u003E\u003Cli\u003ERole name: \u003Ccode\u003Einsurance-claims-iceberg-data-role\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EAttach the \u003Ccode\u003Einsurance-claims-iceberg-data-policy\u003C/code\u003E created above\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate role\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003ELake Formation Setup\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This section uses \u003Ccode\u003EACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\u003C/code\u003E in your Snowflake catalog integration. Vended credentials allow Snowflake to obtain temporary, scoped credentials from Lake Formation to access S3 data directly.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEnable Lake Formation\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAWS Console → Lake Formation → Get started\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EAccept the default settings to initialize Lake Formation in your account\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERegister the S3 Data Lake Location\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ELake Formation → Data lake locations → Register location\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ES3 path: \u003Ccode\u003Es3://<account-id>-us-west-2-insurance-claims-iceberg-data/\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIAM role: Select \u003Ccode\u003Einsurance-claims-iceberg-data-role\u003C/code\u003E (created in the previous step)\u003C/li\u003E\u003Cli\u003EPermission mode: Select \u003Cstrong\u003ELake Formation\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERegister location\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/register_data_lake_location.png?v=a3cd9659\" alt=\"Register Data Lake Location\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGrant Data Permissions to the IAM Role\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ELake Formation → Data permissions → Grant\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EPrincipal type: \u003Cstrong\u003EIAM users and roles\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EIAM role: \u003Ccode\u003Einsurance-claims-iceberg-data-role\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EDatabase: \u003Ccode\u003Einsurance_claims_iceberg_glue_db\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ETable: \u003Cstrong\u003EAll tables\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ETable permissions: \u003Cstrong\u003ESelect\u003C/strong\u003E, \u003Cstrong\u003EDescribe\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGrant\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Grant_Permissions_on_Glue_Database_to_Snowflake_Role_Part_1.png?v=a3cd9659\" alt=\"Grant Permissions on Glue Database 1\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Grant_Permissions_on_Glue_Database_to_Snowflake_Role_Part_2.png?v=a3cd9659\" alt=\"Grant Permissions on Glue Database 2\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Grant_Permissions_on_Glue_Database_to_Snowflake_Role_Part_3.png?v=a3cd9659\" alt=\"Grant Permissions on Glue Database 3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERegister the S3 Data Location\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ELake Formation → Data locations → Grant\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EStorage locations: \u003Ccode\u003Es3://<account-id>-us-west-2-insurance-claims-iceberg-data/\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIAM users and roles: Select \u003Ccode\u003Einsurance-claims-iceberg-data-role\u003C/code\u003E (created in the previous step)\u003C/li\u003E\u003Cli\u003ERegistered account location should be the \u003Cstrong\u003EAWS Account ID\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECheckmark \u003Cstrong\u003EGrantable\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGrant\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/register_data_location.png?v=a3cd9659\" alt=\"Register Data Location\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVerify Lake Formation Permissions\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ELake Formation → Data permissions\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EConfirm that \u003Ccode\u003Einsurance-claims-iceberg-data-role\u003C/code\u003E has all the permissions on all tables in \u003Ccode\u003Einsurance_claims_iceberg_glue_db\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 2: Connect Snowflake to AWS Glue via Catalog Integration\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate the Catalog Integration\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ERun the following SQL in Snowflake (via Snowflake CoCo CLI, Snowsight, or SnowSQL). Replace the placeholder values with your AWS account details:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CATALOG INTEGRATION glue_db_catalog_integration\n  CATALOG_SOURCE = ICEBERG_REST\n  TABLE_FORMAT = ICEBERG\n  REST_CONFIG = (\n    CATALOG_URI = 'https://glue.<region>.amazonaws.com/iceberg'\n    CATALOG_API_TYPE = AWS_GLUE\n    CATALOG_NAME = '<12_digit_aws_account_id>'\n    ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\n  )\n  REST_AUTHENTICATION = (\n    TYPE = SIGV4\n    SIGV4_IAM_ROLE = 'arn:aws:iam::<account_id>:role/insurance-claims-iceberg-data-role'\n    SIGV4_SIGNING_REGION = '<region>'\n  )\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERetrieve Snowflake's IAM User ARN and External ID\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ERun the following to get the values needed for the AWS trust policy:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESCRIBE CATALOG INTEGRATION glue_db_catalog_integration;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote the values for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIAM_USER_ARN\u003C/strong\u003E — e.g., \u003Ccode\u003Earn:aws:iam::<aws-account-id>:user/gqx31000-s\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEXTERNAL_ID\u003C/strong\u003E — e.g., \u003Ccode\u003EIWB00000_SFCRole=7_Hz7Ebsyt1q+08P9P7XXXXXXX=\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUpdate the AWS IAM Trust Policy\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EGo back to \u003Cstrong\u003EAWS Console → IAM → Roles → \u003Ccode\u003Einsurance-claims-iceberg-data-role\u003C/code\u003E → Trust relationships → Edit trust policy\u003C/strong\u003E and replace the \u003Ccode\u003E<SNOWFLAKE_IAM_USER_ARN>\u003C/code\u003E and \u003Ccode\u003E<SNOWFLAKE_EXTERNAL_ID>\u003C/code\u003E placeholders with the values from the previous step.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n    "Version": "2012-10-17",\n    "Statement": [\n        {\n            "Sid": "SnowflakeAccess",\n            "Effect": "Allow",\n            "Principal": {\n                "AWS": "<SNOWFLAKE_IAM_USER_ARN>"\n            },\n            "Action": "sts:AssumeRole",\n            "Condition": {\n                "StringEquals": {\n                    "sts:ExternalId": "<SNOWFLAKE_EXTERNAL_ID>"\n                }\n            }\n        },\n        {\n            "Sid": "GlueAndLakeFormationAccess",\n            "Effect": "Allow",\n            "Principal": {\n                "Service": [\n                    "lakeformation.amazonaws.com",\n                    "glue.amazonaws.com"\n                ]\n            },\n            "Action": [\n                "sts:AssumeRole",\n                "sts:SetContext"\n            ]\n        }\n    ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVerify the Integration\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$VERIFY_CATALOG_INTEGRATION('glue_db_catalog_integration');\nSELECT SYSTEM$LIST_NAMESPACES_FROM_CATALOG('glue_db_catalog_integration');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EThe first command should return a success status confirming Snowflake can connect to AWS Glue\u003C/li\u003E\u003Cli\u003EThe second command should list the \u003Ccode\u003Einsurance_claims_iceberg_glue_db\u003C/code\u003E namespace\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 3: Provision Iceberg Tables & Snowflake Objects (Database, Schema, Stages, Tables)\u003C/h3\u003E\n","\u003Cp\u003EClone the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts\"\u003E\u003Ccode\u003Egit repository\u003C/code\u003E\u003C/a\u003E into your local machine and navigate to the folder \u003Cstrong\u003Esite/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EAll SQL commands for this step are in \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/setup.sql\"\u003E\u003Ccode\u003Eassets/setup.sql\u003C/code\u003E\u003C/a\u003E.Run the following prompt using Snowflake CoCo CLI (replace the AWS ACCOUNT ID with yours.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003ERun the setup.sql script by replacing the <account-id> with <01234554634> and access the data to upload into the stage from the data folder in this path\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/setup_script_with_coco_cli.gif?v=a3cd9659\" alt=\"Run Setup Script with CoCo CLI\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe setup script performs the following sub-steps in order:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESub-step\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it does\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3a\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates the \u003Ccode\u003EINSURANCE_CLAIMS_WH\u003C/code\u003E warehouse, \u003Ccode\u003EINSURANCE_CLAIMS_INSIGHTS_DB\u003C/code\u003E database, \u003Ccode\u003ECLAIMS_ANALYTICS\u003C/code\u003E schema, and two stages (\u003Ccode\u003EDATA_STAGE\u003C/code\u003E, \u003Ccode\u003EML_MODELS\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3b\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates the Catalog Linked Database (\u003Ccode\u003Eglue_database_linked_db\u003C/code\u003E) that connects to AWS Glue via the catalog integration from Step 2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3c\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUploads sample CSV/Parquet data files and the fraud detection model to the Snowflake stages using \u003Ccode\u003EPUT\u003C/code\u003E commands\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3d\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates three Iceberg tables (\u003Ccode\u003Efnol_reports\u003C/code\u003E, \u003Ccode\u003Eadjuster_notes\u003C/code\u003E, \u003Ccode\u003Einitial_estimates\u003C/code\u003E) in the linked database with S3 base locations and auto-refresh enabled\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3e\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoads the staged Parquet files into the Iceberg tables using \u003Ccode\u003ECOPY INTO\u003C/code\u003E with column name matching\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3f\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates two Snowflake native tables (\u003Ccode\u003EFRAUD_FLAGS\u003C/code\u003E, \u003Ccode\u003EPOLICY_DETAILS\u003C/code\u003E) in the \u003Ccode\u003ECLAIMS_ANALYTICS\u003C/code\u003E schema\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3g\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoads the staged CSV files into the native tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3h\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERuns verification queries to confirm row counts across all five tables\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EStep 4: Run Feature Engineering with Snowpark Connect for Apache Spark\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a Notebook in Snowsight\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ESnowsight → Notebooks → Create Notebook\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUpload the notebook from the cloned repository located in \u003Cstrong\u003Eassets\u003C/strong\u003E folder\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/upload_notebook_into_snowsight.png?v=a3cd9659\" alt=\"Upload Notebook\"\u003E\u003C/li\u003E\u003Cli\u003EAlso, Upload the \u003Ccode\u003Efraud_model.py\u003C/code\u003E script to the notebook environment\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInstall the Snowpark Connect package\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the notebook, install the \u003Ccode\u003Esnowpark-connect\u003C/code\u003E package from the Packages panel\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/install_packages_into_notebook.png?v=a3cd9659\" alt=\"Install Package in Notebook\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun the Feature Engineering notebook\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERun through \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/Insurance-Claims-Feature-Engineering.ipynb\"\u003E\u003Ccode\u003EInsurance-Claims-Feature-Engineering.ipynb\u003C/code\u003E\u003C/a\u003E notebook from SnowSight UI\u003C/li\u003E\u003Cli\u003EThis notebook reads from both Iceberg tables (via Catalog Linked Database) and Native tables, performs feature engineering and fraud scoring, and writes the enriched output to the \u003Ccode\u003ECLAIMS_PROCESSED_FEATURES\u003C/code\u003E table\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 5: Enable Cortex Analyst, Cortex Agent, and Snowflake CoWork\u003C/h3\u003E\n","\u003Cp\u003ERun through the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/cortex_analyst_snowflake_intelligence_setup.sql\"\u003E\u003Ccode\u003Ecortex_analyst_snowflake_intelligence_setup.sql\u003C/code\u003E\u003C/a\u003E script using CoCo CLI with the following prompt:\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003ERun the cortex_analyst_snowflake_intelligence_setup.sql script to create the semantic view by uploading the claims_semantic_view.yaml from assets folder, setup Cortex Analyst, Cortex Agent and enable Snowflake CoWork\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EThis runs through the following steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EUpload the semantic model YAML\u003C/strong\u003E to \u003Ccode\u003ESEMANTIC_MODEL_STAGE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECreate the Semantic View\u003C/strong\u003E using \u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECreate the Snowflake CoWork database and schema\u003C/strong\u003E (\u003Ccode\u003ESNOWFLAKE_INTELLIGENCE.AGENTS\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECreate the Cortex Agent\u003C/strong\u003E with the \u003Ccode\u003Ecortex_analyst_text_to_sql\u003C/code\u003E tool pointing to the semantic view\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGrant access permissions\u003C/strong\u003E to the agent, semantic view, and underlying table\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 6: Query Claims Data with Snowflake CoWork\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EVerify\u003C/strong\u003E by navigating to \u003Cstrong\u003ESnowsight → AI & ML → Snowflake CoWork\u003C/strong\u003E and selecting the "Insurance Claims Analyst" agent\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAsk Questions\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003E"Show me all high risk fraud claims"\u003C/li\u003E\u003Cli\u003E"What is the total claim amount by loss type?"\u003C/li\u003E\u003Cli\u003E"How many claims are under SIU investigation?"\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/snowflake_intelligence_claims_data_questions.gif?v=a3cd9659\" alt=\"Run Snowflake  Intelligence\"\u003E\u003C/p\u003E"],"description":"","title":"Unlocking Hidden Value: The Modern Data Architecture for AI-Powered Insurance Claims Insights",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nThis guide walks through how to build a modern data architecture on Snowflake and AWS that reads open table format Iceberg data from AWS and combines it with Snowflake Native tables, transforms and enriches the data using Snowpark Connect for Apache Spark, and delivers AI-powered insights through Cortex Analyst and Snowflake CoWork. It provides an end-to-end blueprint for combining open table formats, feature engineering, and natural language analytics to accelerate fraud detection and claims processing.\n\n## Prerequisites\n\n- Snowflake account with ACCOUNTADMIN role\n- AWS account with permissions to create and manage S3 buckets, IAM roles/policies, AWS Glue Data Catalog, and Lake Formation\n- [Snowflake CoCo CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) (CoCo) installed and configured with a valid connection to the Snowflake account\n\n## What You'll Learn\n\n- **Snowflake Horizon Catalog** — How the unified catalog now enables discovery and governance of both external Apache Iceberg tables and Snowflake Native tables from a single pane of glass, without moving data\n- **Snowpark Connect for Apache Spark** — How to use familiar PySpark APIs to join, enrich, and score data across external Iceberg tables and internal Native tables in a single Spark job running on Snowflake compute\n- **Snowflake CoWork** — How to publish a Cortex Agent that lets business users ask natural language questions on the aggregated, fraud-scored claims data without writing SQL\n- **Snowflake CoCo (CoCo)** — How to use CoCo and interactively deploy the entire solution end-to-end, from creating Snowflake objects and running Spark jobs to setting up Cortex Analyst and Snowflake CoWork\n\n\n## Solution Architecture\n\n![Solution Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Modern_Data_Architecture_for_AI-Powered_Insurance_Claims_Insights.jpeg?v=a3cd9659)\n\n## Architecture Overview\n\nThis solution leverages AWS and Snowflake services to create an end-to-end data pipeline for insurance claims analytics.\n\n### Components\n\n**AWS Account (US-West-2)**\n- **Amazon S3** - Raw data storage in Apache Iceberg format\n- **AWS Glue Data Catalog** - Metadata management for Iceberg tables\n\n**Snowflake (US-West-2)**\n- **Catalog Linked Database** - Connects to AWS Glue Catalog for accessing external Iceberg data\n- **Snowflake Native Tables** - Persistent storage for transformed data\n- **Snowflake Horizon Catalog** - Central governance layer\n- **Snowpark Connect for Apache Spark** - Data transformation and feature engineering\n- **Snowflake CoWork** - AI-powered agent interactions\n- **Cortex Analyst** - Natural language to SQL generation\n\n### Phase 1: Data Ingestion & External Cataloging (AWS to Snowflake)\n\n| Step | Component | Claims Scenario |\n|------|-----------|-----------------|\n| Claims Data Landing | Amazon S3 | Raw claims data (e.g., FNOL reports, adjuster notes, images, etc.) is stored in the data lake using Apache Iceberg table format |\n| Metadata Registration | AWS Glue Data Catalog | The Glue Catalog is updated with Iceberg table metadata, acting as the \"source of truth\" pointer to the Iceberg data files in S3 |\n| Catalog Synchronization | Catalog Linked Database | Snowflake's Catalog Linked Database connects to AWS Glue Catalog, ingesting the Iceberg metadata and creating a schema and table that points directly to the external Iceberg data in S3 |\n\n### Phase 2: Data Transformation & Feature Engineering\n\n| Step | Component | Claims Scenario |\n|------|-----------|-----------------|\n| Data Processing & Feature Creation | Snowpark Connect for Apache Spark | The Spark job reads claims data from the Catalog Linked Database, performs ML-based feature engineering (e.g., NLP for claims description, embeddings, calculating loss ratios, geo-spatial analysis, etc.) and writes the enriched features to a Snowflake Native Table using the Iceberg REST API |\n| Data Persistence | Snowpark Connect for Apache Spark (Write-back) | The resulting enriched and curated data is written back to a new Snowflake Native Table. This table is optimized for querying and AI/ML workloads |\n\n### Phase 3: Insight Generation & Agent Interaction\n\n| Step | Component | Claims Scenario |\n|------|-----------|-----------------|\n| Agent Query & Query Orchestration | Snowflake CoWork, Cortex Agents | A claims agent, without writing SQL, asks a natural language question (e.g., \"Show me the top 5 high-risk claims filed this week\"). Cortex Analyst receives the request, understands the context, and generates the appropriate SQL query to Cortex Analyst |\n| SQL Generation & Execution | Cortex Analyst | The Cortex Analyst component receives a user question, translates it into an optimized Snowflake SQL query, and executes it against the Snowflake Native Tables |\n| Insight Delivery | Snowflake CoWork | The end-to-end flow delivers instant answers to complex analytical questions, allowing users to act quickly by converting them to summary alerts or remediation actions |\n\n### Cross-Cutting Layer: Governance\n\nThe **Snowflake Horizon Catalog** acts as the central governance layer throughout this entire process, ensuring every user and component (from Snowpark job to Snowflake CoWork) only accesses data according to defined security policies.\n\n## Solution Deployment\n\n### Step 1: Configure AWS Infrastructure (S3, Glue, IAM, Lake Formation)\n\n#### S3 Bucket for Iceberg Data Files\n\n1. Navigate to **AWS Console → S3 → Create bucket**\n2. Enter the bucket name: `\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data`\n3. Select the AWS Region: `us-west-2` (must match your Glue catalog region)\n4. Leave **Block all public access** enabled (default)\n5. Enable **Bucket Versioning** (recommended for Iceberg table consistency)\n6. Click **Create bucket**\n7. Once created, create the following folder structure inside the bucket:\n   - `fnol_reports/` — First Notice of Loss reports\n   - `adjuster_notes/` — Adjuster notes and assessments\n   - `initial_estimates/` — Initial claim estimates\n  \nThe folder structure should look as below once the S3 Bucket and Prefixes are created\n![S3 Bucket with Prefixes](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/s3-bucket-prefixes.png?v=a3cd9659)\n\n#### AWS Glue Data Catalog\n\n1. Verify that the Glue Data Catalog is enabled in your region (enabled by default in most regions)\n   - Navigate to **AWS Console → Glue → Data Catalog → Databases**\n2. Create a Glue Database\n   - Navigate to **AWS Console → Glue → Databases → Add database**\n   - Database name: `insurance_claims_iceberg_glue_db`\n   - Location: `s3://\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data/`\n   - Click **Create database**\n![Glue Database](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/create-glue-database.png?v=a3cd9659) \n\n#### IAM Role with Glue/Lake Formation Permissions\n\n1. **Create an IAM Policy**\n   - Navigate to **AWS Console → IAM → Policies → Create policy**\n   - Select the **JSON** tab and paste the following policy. Update the `account-id` placeholder before saving the policy.\n   - Policy name: `insurance-claims-iceberg-data-policy`\n\n   ```json\n   {\n       \"Version\": \"2012-10-17\",\n       \"Statement\": [\n           {\n               \"Sid\": \"GlueAndS3ScopedAccess\",\n               \"Effect\": \"Allow\",\n               \"Action\": [\n                   \"glue:CreateTable\",\n                   \"glue:DeleteDatabase\",\n                   \"glue:GetTables\",\n                   \"glue:GetPartitions\",\n                   \"glue:UpdateTable\",\n                   \"glue:DeleteTable\",\n                   \"glue:GetDatabases\",\n                   \"glue:GetTable\",\n                   \"glue:GetDatabase\",\n                   \"glue:GetPartition\",\n                   \"glue:GetCatalogs\",\n                   \"glue:CreateDatabase\",\n                   \"glue:GetCatalog\",\n                   \"s3:ListBucket\",\n                   \"s3:GetBucketLocation\"\n               ],\n               \"Resource\": [\n                   \"arn:aws:glue:*:*:table/*/*\",\n                   \"arn:aws:glue:*:*:catalog\",\n                   \"arn:aws:glue:*:*:database/insurance_claims_iceberg_glue_db\",\n                   \"arn:aws:s3:::\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data\"\n               ]\n           },\n           {\n               \"Sid\": \"GlueAndS3GlobalAccess\",\n               \"Effect\": \"Allow\",\n               \"Action\": [\n                   \"glue:GetTables\",\n                   \"glue:GetCatalog\",\n                   \"glue:GetTable\",\n                   \"s3:ListAllMyBuckets\",\n                   \"s3:AbortMultipartUpload\",\n                   \"s3:ListMultipartUploadParts\",\n                   \"s3:GetBucketCors\",\n                   \"s3:GetBucketVersioning\",\n                   \"s3:GetBucketAcl\",\n                   \"s3:GetBucketNotification\",\n                   \"s3:GetBucketPolicy\",\n                   \"lakeformation:GetDataAccess\"\n               ],\n               \"Resource\": \"*\"\n           },\n           {\n               \"Sid\": \"S3DataAccess\",\n               \"Effect\": \"Allow\",\n               \"Action\": [\n                   \"s3:GetObject\",\n                   \"s3:PutObject\",\n                   \"s3:DeleteObject\",\n                   \"s3:ListBucket\",\n                   \"s3:GetBucketLocation\",\n                   \"s3:ListAllMyBuckets\"\n               ],\n               \"Resource\": [\n                   \"arn:aws:s3:::\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data\",\n                   \"arn:aws:s3:::\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data/*\"\n               ]\n           }\n       ]\n   }\n   ```\n\n2. **Create an IAM Role**\n   - Navigate to **AWS Console → IAM → Roles → Create role**\n   - Trusted entity type: **AWS account**\n   - Select **Another AWS account** (enter a placeholder account ID for now; this will be updated after the Snowflake catalog integration is created)\n   - Role name: `insurance-claims-iceberg-data-role`\n   - Attach the `insurance-claims-iceberg-data-policy` created above\n   - Click **Create role**\n\n#### Lake Formation Setup\n\n\u003E **Note:** This section uses `ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS` in your Snowflake catalog integration. Vended credentials allow Snowflake to obtain temporary, scoped credentials from Lake Formation to access S3 data directly.\n\n1. **Enable Lake Formation**\n   - Navigate to **AWS Console → Lake Formation → Get started**\n   - Accept the default settings to initialize Lake Formation in your account\n\n2. **Register the S3 Data Lake Location**\n   - Navigate to **Lake Formation → Data lake locations → Register location**\n   - S3 path: `s3://\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data/`\n   - IAM role: Select `insurance-claims-iceberg-data-role` (created in the previous step)\n   - Permission mode: Select **Lake Formation**\n   - Click **Register location**\n\n   ![Register Data Lake Location](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/register_data_lake_location.png?v=a3cd9659)\n\n3. **Grant Data Permissions to the IAM Role**\n   - Navigate to **Lake Formation → Data permissions → Grant**\n   - Principal type: **IAM users and roles**\n   - IAM role: `insurance-claims-iceberg-data-role`\n   - Database: `insurance_claims_iceberg_glue_db`\n   - Table: **All tables**\n   - Table permissions: **Select**, **Describe**\n   - Click **Grant**\n\n   ![Grant Permissions on Glue Database 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Grant_Permissions_on_Glue_Database_to_Snowflake_Role_Part_1.png?v=a3cd9659)\n   ![Grant Permissions on Glue Database 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Grant_Permissions_on_Glue_Database_to_Snowflake_Role_Part_2.png?v=a3cd9659)\n   ![Grant Permissions on Glue Database 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/Grant_Permissions_on_Glue_Database_to_Snowflake_Role_Part_3.png?v=a3cd9659)\n\n2. **Register the S3 Data Location**\n   - Navigate to **Lake Formation → Data locations → Grant**\n   - Storage locations: `s3://\u003Caccount-id\u003E-us-west-2-insurance-claims-iceberg-data/`\n   - IAM users and roles: Select `insurance-claims-iceberg-data-role` (created in the previous step)\n   - Registered account location should be the **AWS Account ID**\n   - Checkmark **Grantable**\n   - Click **Grant**\n\n   ![Register Data Location](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/register_data_location.png?v=a3cd9659)\n\n6. **Verify Lake Formation Permissions**\n   - Navigate to **Lake Formation → Data permissions**\n   - Confirm that `insurance-claims-iceberg-data-role` has all the permissions on all tables in `insurance_claims_iceberg_glue_db`\n\n### Step 2: Connect Snowflake to AWS Glue via Catalog Integration\n\n1. **Create the Catalog Integration**\n\n   Run the following SQL in Snowflake (via Snowflake CoCo CLI, Snowsight, or SnowSQL). Replace the placeholder values with your AWS account details:\n\n   ```sql\n   CREATE OR REPLACE CATALOG INTEGRATION glue_db_catalog_integration\n     CATALOG_SOURCE = ICEBERG_REST\n     TABLE_FORMAT = ICEBERG\n     REST_CONFIG = (\n       CATALOG_URI = 'https://glue.\u003Cregion\u003E.amazonaws.com/iceberg'\n       CATALOG_API_TYPE = AWS_GLUE\n       CATALOG_NAME = '\u003C12_digit_aws_account_id\u003E'\n       ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS\n     )\n     REST_AUTHENTICATION = (\n       TYPE = SIGV4\n       SIGV4_IAM_ROLE = 'arn:aws:iam::\u003Caccount_id\u003E:role/insurance-claims-iceberg-data-role'\n       SIGV4_SIGNING_REGION = '\u003Cregion\u003E'\n     )\n     ENABLED = TRUE;\n   ```\n\n2. **Retrieve Snowflake's IAM User ARN and External ID**\n\n   Run the following to get the values needed for the AWS trust policy:\n\n   ```sql\n   DESCRIBE CATALOG INTEGRATION glue_db_catalog_integration;\n   ```\n\n   Note the values for:\n   - **IAM_USER_ARN** — e.g., `arn:aws:iam::\u003Caws-account-id\u003E:user/gqx31000-s`\n   - **EXTERNAL_ID** — e.g., `IWB00000_SFCRole=7_Hz7Ebsyt1q+08P9P7XXXXXXX=`\n\n3. **Update the AWS IAM Trust Policy**\n\n   Go back to **AWS Console → IAM → Roles → `insurance-claims-iceberg-data-role` → Trust relationships → Edit trust policy** and replace the `\u003CSNOWFLAKE_IAM_USER_ARN\u003E` and `\u003CSNOWFLAKE_EXTERNAL_ID\u003E` placeholders with the values from the previous step.\n\n   ```json\n   {\n       \"Version\": \"2012-10-17\",\n       \"Statement\": [\n           {\n               \"Sid\": \"SnowflakeAccess\",\n               \"Effect\": \"Allow\",\n               \"Principal\": {\n                   \"AWS\": \"\u003CSNOWFLAKE_IAM_USER_ARN\u003E\"\n               },\n               \"Action\": \"sts:AssumeRole\",\n               \"Condition\": {\n                   \"StringEquals\": {\n                       \"sts:ExternalId\": \"\u003CSNOWFLAKE_EXTERNAL_ID\u003E\"\n                   }\n               }\n           },\n           {\n               \"Sid\": \"GlueAndLakeFormationAccess\",\n               \"Effect\": \"Allow\",\n               \"Principal\": {\n                   \"Service\": [\n                       \"lakeformation.amazonaws.com\",\n                       \"glue.amazonaws.com\"\n                   ]\n               },\n               \"Action\": [\n                   \"sts:AssumeRole\",\n                   \"sts:SetContext\"\n               ]\n           }\n       ]\n   }\n   ```\n\n5. **Verify the Integration**\n\n   ```sql\n   SELECT SYSTEM$VERIFY_CATALOG_INTEGRATION('glue_db_catalog_integration');\n   SELECT SYSTEM$LIST_NAMESPACES_FROM_CATALOG('glue_db_catalog_integration');\n   ```\n\n   - The first command should return a success status confirming Snowflake can connect to AWS Glue\n   - The second command should list the `insurance_claims_iceberg_glue_db` namespace\n\n### Step 3: Provision Iceberg Tables & Snowflake Objects (Database, Schema, Stages, Tables)\n\nClone the [`git repository`](https://github.com/Snowflake-Labs/sfquickstarts) into your local machine and navigate to the folder **site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/**\n\n\u003E All SQL commands for this step are in [`assets/setup.sql`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/setup.sql).Run the following prompt using Snowflake CoCo CLI (replace the AWS ACCOUNT ID with yours.\n\n`Run the setup.sql script by replacing the \u003Caccount-id\u003E with \u003C01234554634\u003E and access the data to upload into the stage from the data folder in this path`\n\n ![Run Setup Script with CoCo CLI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/setup_script_with_coco_cli.gif?v=a3cd9659)\n\nThe setup script performs the following sub-steps in order:\n\n| Sub-step | What it does |\n|----------|-------------|\n| **3a** | Creates the `INSURANCE_CLAIMS_WH` warehouse, `INSURANCE_CLAIMS_INSIGHTS_DB` database, `CLAIMS_ANALYTICS` schema, and two stages (`DATA_STAGE`, `ML_MODELS`) |\n| **3b** | Creates the Catalog Linked Database (`glue_database_linked_db`) that connects to AWS Glue via the catalog integration from Step 2 |\n| **3c** | Uploads sample CSV/Parquet data files and the fraud detection model to the Snowflake stages using `PUT` commands |\n| **3d** | Creates three Iceberg tables (`fnol_reports`, `adjuster_notes`, `initial_estimates`) in the linked database with S3 base locations and auto-refresh enabled |\n| **3e** | Loads the staged Parquet files into the Iceberg tables using `COPY INTO` with column name matching |\n| **3f** | Creates two Snowflake native tables (`FRAUD_FLAGS`, `POLICY_DETAILS`) in the `CLAIMS_ANALYTICS` schema |\n| **3g** | Loads the staged CSV files into the native tables |\n| **3h** | Runs verification queries to confirm row counts across all five tables |\n\n### Step 4: Run Feature Engineering with Snowpark Connect for Apache Spark\n\n1. **Create a Notebook in Snowsight**\n   - Navigate to **Snowsight → Notebooks → Create Notebook**\n   - Upload the notebook from the cloned repository located in **assets** folder\n    ![Upload Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/upload_notebook_into_snowsight.png?v=a3cd9659)\n   - Also, Upload the `fraud_model.py` script to the notebook environment\n\n2. **Install the Snowpark Connect package**\n   - In the notebook, install the `snowpark-connect` package from the Packages panel\n     ![Install Package in Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/install_packages_into_notebook.png?v=a3cd9659)\n\n3. **Run the Feature Engineering notebook**\n   - Run through [`Insurance-Claims-Feature-Engineering.ipynb`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/Insurance-Claims-Feature-Engineering.ipynb) notebook from SnowSight UI\n   - This notebook reads from both Iceberg tables (via Catalog Linked Database) and Native tables, performs feature engineering and fraud scoring, and writes the enriched output to the `CLAIMS_PROCESSED_FEATURES` table\n\n### Step 5: Enable Cortex Analyst, Cortex Agent, and Snowflake CoWork\n\nRun through the [`cortex_analyst_snowflake_intelligence_setup.sql`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/modern-data-architecture-for-ai-powered-insurance-claims-insights/assets/cortex_analyst_snowflake_intelligence_setup.sql) script using CoCo CLI with the following prompt:\n\n`Run the cortex_analyst_snowflake_intelligence_setup.sql script to create the semantic view by uploading the claims_semantic_view.yaml from assets folder, setup Cortex Analyst,\nCortex Agent and enable Snowflake CoWork` \n\nThis runs through the following steps:\n\n1. **Upload the semantic model YAML** to `SEMANTIC_MODEL_STAGE`\n2. **Create the Semantic View** using `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML`\n3. **Create the Snowflake CoWork database and schema** (`SNOWFLAKE_INTELLIGENCE.AGENTS`)\n4. **Create the Cortex Agent** with the `cortex_analyst_text_to_sql` tool pointing to the semantic view\n5. **Grant access permissions** to the agent, semantic view, and underlying table\n\n### Step 6: Query Claims Data with Snowflake CoWork\n\n1. **Verify** by navigating to **Snowsight → AI & ML → Snowflake CoWork** and selecting the \"Insurance Claims Analyst\" agent\n2. **Ask Questions**\n   - \"Show me all high risk fraud claims\"\n   - \"What is the total claim amount by loss type?\"\n   - \"How many claims are under SIU investigation?\"\n\n\n ![Run Snowflake  Intelligence](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/modern-data-architecture-for-ai-powered-insurance-claims-insights/snowflake_intelligence_claims_data_questions.gif?v=a3cd9659)\n \n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-687b40f2a3","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c98faeace3",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3bfce69a2a","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-26",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-493fa66b69","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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