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+---
+title: Querying S3 Tables with Snowflake
+description: In this tutorial, you will learn how to integrate AWS S3 Tables with Snowflake to query Iceberg tables stored in S3 Tables buckets through LocalStack.
+template: doc
+nav: 
+label: 
+---
+
+## Introduction
+
+In this tutorial, you will explore how to connect Snowflake to AWS S3 Tables locally using LocalStack. S3 Tables is a managed Apache Iceberg table catalog that uses S3 storage, providing built-in maintenance features like automatic compaction and snapshot management.
+
+With LocalStack's Snowflake emulator, you can create catalog integrations that connect to S3 Tables and query Iceberg tables without needing cloud resources. This integration allows you to:
+
+- Create catalog integrations to connect Snowflake to S3 Tables.
+- Query existing Iceberg tables stored in S3 Tables buckets.
+- Leverage automatic schema inference from external Iceberg tables.
+
+## Prerequisites
+
+- [`localstack` CLI](/snowflake/getting-started/) with a [`LOCALSTACK_AUTH_TOKEN`](/aws/getting-started/auth-token/)
+- [LocalStack for Snowflake](/snowflake/getting-started/)
+- [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) & [`awslocal` wrapper](/aws/integrations/aws-native-tools/aws-cli/#localstack-aws-cli-awslocal)
+- Python 3.10+ with `pyiceberg` and `pyarrow` installed
+
+## Start LocalStack
+
+Start your LocalStack container with the Snowflake emulator enabled.
+
+```bash
+export LOCALSTACK_AUTH_TOKEN=<your_auth_token>
+localstack start --stack snowflake
+```
+
+## Create S3 Tables resources
+
+Before configuring Snowflake, you need to create S3 Tables resources using the AWS CLI. This includes a table bucket and a namespace.
+
+### Create a table bucket
+
+Create a table bucket to store your Iceberg tables.
+
+```bash
+awslocal s3tables create-table-bucket --name my-table-bucket
+```
+
+```bash title="Output"
+{
+    "arn": "arn:aws:s3tables:us-east-1:000000000000:bucket/my-table-bucket"
+}
+```
+
+### Create a namespace
+
+Create a namespace within the table bucket to organize your tables.
+
+```bash
+awslocal s3tables create-namespace \
+    --table-bucket-arn arn:aws:s3tables:us-east-1:000000000000:bucket/my-table-bucket \
+    --namespace my_namespace
+```
+
+```bash title="Output"
+{
+    "tableBucketARN": "arn:aws:s3tables:us-east-1:000000000000:bucket/my-table-bucket",
+    "namespace": [
+        "my_namespace"
+    ]
+}
+```
+
+## Create and populate a table in S3 Tables
+
+To query data from Snowflake using `CATALOG_TABLE_NAME`, the S3 Tables table must have a defined schema and contain data. Use PyIceberg to create a table with schema and populate it with data.
+
+First, install the required Python packages:
+
+```bash
+pip install "pyiceberg[s3fs,pyarrow]" boto3
+```
+
+Create a Python script named `setup_s3_tables.py` with the following content:
+
+```python
+import pyarrow as pa
+from pyiceberg.catalog.rest import RestCatalog
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, LongType
+
+# Configuration
+LOCALSTACK_URL = "http://localhost.localstack.cloud:4566"
+S3TABLES_URL = "http://s3tables.localhost.localstack.cloud:4566"
+TABLE_BUCKET_NAME = "my-table-bucket"
+NAMESPACE = "my_namespace"
+TABLE_NAME = "customer_orders"
+REGION = "us-east-1"
+
+# Create PyIceberg REST catalog pointing to S3 Tables
+catalog = RestCatalog(
+    name="s3tables_catalog",
+    uri=f"{S3TABLES_URL}/iceberg",
+    warehouse=TABLE_BUCKET_NAME,
+    **{
+        "s3.region": REGION,
+        "s3.endpoint": LOCALSTACK_URL,
+        "client.access-key-id": "000000000000",
+        "client.secret-access-key": "test",
+        "rest.sigv4-enabled": "true",
+        "rest.signing-name": "s3tables",
+        "rest.signing-region": REGION,
+    },
+)
+
+# Define table schema
+schema = Schema(
+    NestedField(field_id=1, name="order_id", field_type=StringType(), required=False),
+    NestedField(field_id=2, name="customer_name", field_type=StringType(), required=False),
+    NestedField(field_id=3, name="amount", field_type=LongType(), required=False),
+)
+
+# Create table in S3 Tables
+catalog.create_table(
+    identifier=(NAMESPACE, TABLE_NAME),
+    schema=schema,
+)
+
+print(f"Created table: {NAMESPACE}.{TABLE_NAME}")
+
+# Reload the table to get the latest metadata
+table = catalog.load_table((NAMESPACE, TABLE_NAME))
+
+# Populate table with sample data
+data = pa.table({
+    "order_id": ["ORD001", "ORD002", "ORD003"],
+    "customer_name": ["Alice", "Bob", "Charlie"],
+    "amount": [100, 250, 175],
+})
+
+table.append(data)
+print("Inserted sample data into table")
+
+# Verify table exists
+tables = catalog.list_tables(NAMESPACE)
+print(f"Tables in namespace: {tables}")
+```
+
+Run the script to create the table and populate it with data:
+
+```bash
+python setup_s3_tables.py
+```
+
+```bash title="Output"
+Created table: my_namespace.customer_orders
+Inserted sample data into table
+Tables in namespace: [('my_namespace', 'customer_orders')]
+```
+
+## Connect to the Snowflake emulator
+
+Connect to the locally running Snowflake emulator using an SQL client of your choice (such as DBeaver). The Snowflake emulator runs on `snowflake.localhost.localstack.cloud`.
+
+You can use the following connection parameters:
+
+| Parameter | Value |
+|-----------|-------|
+| Host | `snowflake.localhost.localstack.cloud` |
+| User | `test` |
+| Password | `test` |
+| Account | `test` |
+| Warehouse | `test` |
+
+## Create a catalog integration
+
+Create a catalog integration to connect Snowflake to your S3 Tables bucket. The catalog integration defines how Snowflake connects to the external Iceberg REST catalog provided by S3 Tables.
+
+```sql
+CREATE OR REPLACE CATALOG INTEGRATION s3tables_catalog_integration
+    CATALOG_SOURCE=ICEBERG_REST
+    TABLE_FORMAT=ICEBERG
+    CATALOG_NAMESPACE='my_namespace'
+    REST_CONFIG=(
+        CATALOG_URI='http://s3tables.localhost.localstack.cloud:4566/iceberg'
+        CATALOG_NAME='my-table-bucket'
+    )
+    REST_AUTHENTICATION=(
+        TYPE=AWS_SIGV4
+        AWS_ACCESS_KEY_ID='000000000000'
+        AWS_SECRET_ACCESS_KEY='test'
+        AWS_REGION='us-east-1'
+        AWS_SERVICE='s3tables'
+    )
+    ENABLED=TRUE
+    REFRESH_INTERVAL_SECONDS=60;
+```
+
+In the above query:
+
+- `CATALOG_SOURCE=ICEBERG_REST` specifies that the catalog uses the Iceberg REST protocol.
+- `TABLE_FORMAT=ICEBERG` indicates the table format.
+- `CATALOG_NAMESPACE='my_namespace'` sets the default namespace to query tables from.
+- `REST_CONFIG` configures the connection to the LocalStack S3 Tables REST API endpoint.
+- `REST_AUTHENTICATION` configures AWS SigV4 authentication for the S3 Tables service.
+- `REFRESH_INTERVAL_SECONDS=60` sets how often Snowflake refreshes metadata from the catalog.
+
+## Create an Iceberg table referencing S3 Tables
+
+Create an Iceberg table in Snowflake that references the existing S3 Tables table using `CATALOG_TABLE_NAME`. The schema is automatically inferred from the external table.
+
+```sql
+CREATE OR REPLACE ICEBERG TABLE iceberg_customer_orders
+    CATALOG='s3tables_catalog_integration'
+    CATALOG_TABLE_NAME='my_namespace.customer_orders'
+    AUTO_REFRESH=TRUE;
+```
+
+In the above query:
+
+- `CATALOG` references the catalog integration created in the previous step.
+- `CATALOG_TABLE_NAME` specifies the fully-qualified table name in the format `namespace.table_name`.
+- `AUTO_REFRESH=TRUE` enables automatic refresh of table metadata.
+- No column definitions are needed as the schema is inferred from the existing S3 Tables table.
+
+## Query the Iceberg table
+
+You can now query the Iceberg table like any other Snowflake table. The schema (columns) are automatically available from the external table.
+
+```sql
+SELECT * FROM iceberg_customer_orders;
+```
+
+```sql title="Output"
++----------+---------------+--------+
+| order_id | customer_name | amount |
++----------+---------------+--------+
+| ORD001   | Alice         | 100    |
+| ORD002   | Bob           | 250    |
+| ORD003   | Charlie       | 175    |
++----------+---------------+--------+
+```
+
+## Conclusion
+
+In this tutorial, you learned how to integrate AWS S3 Tables with Snowflake using LocalStack. You created S3 Tables resources, populated a table with data using PyIceberg, configured a catalog integration in Snowflake, and queried Iceberg tables stored in S3 Tables buckets using `CATALOG_TABLE_NAME`.
+
+The S3 Tables integration enables you to:
+
+- Query data stored in S3 Tables using familiar Snowflake SQL syntax.
+- Leverage automatic schema inference from external Iceberg catalogs.
+- Develop and test your data lakehouse integrations locally without cloud resources.
+
+LocalStack's Snowflake emulator combined with S3 Tables support provides a complete local environment for developing and testing multi-platform data analytics workflows.