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Showing content from https://docs.snowflake.com/en/developer below:

Write applications that extend Snowflake, act as a client, or act as an integrating component.

SNOWPARK API

Using Snowpark libraries and code execution environments, you can run Python and other programming languages next to your data in Snowflake.

Enable all data users to bring their work to a single platform with native support for Python, Java, Scala, and more.

Apply consistent controls trusted by over 500 of the Forbes Global 2000 across all workloads.

Benefit from the Snowflake Data Cloud with super price/performance and near-zero maintenance.

Run custom Python, Java, or Scala code directly in Snowflake with Snowpark user-defined functions (UDFs) and stored procedures. There are no separate clusters to manage, scale, or operate.

from snowflake.snowpark import Session
from snowflake.snowpark.functions import col
# Create a new session, using the connection properties specified in a file.

new_session = Session.builder.configs(connection_parameters).create()
# Create a DataFrame that contains the id, name, and serial_number

# columns in the “sample_product_data” table.
df = session.table("sample_product_data").select(
col("id"), col("name"), col("name"), col("serial_number")
)
# Show the results 

df.show()

Use the following quickstart tutorials to get a hands-on introduction to Snowpark

Snowflake AI and ML

Snowflake offers two broad categories of features based on generative artificial intelligence (AI) and machine learning (ML).

Understand unstructured data, answer freeform questions, generate accurate text-to-SQL responses, and provide intelligent assistance using large language models (LLMs).

Pre-process data and train, manage, and deploy machine learning models, all within Snowflake.

Snowflake Python APIs

Create and manage Snowflake resources across data engineering, Snowpark, Snowflake ML, and application workloads using a unified, first-class Python API.

NATIVE APPS FRAMEWORK

Expand the capabilities of other Snowflake features by sharing data and related business logic with other Snowflake accounts.

SNOWPARK CONTAINER SERVICES

Build atop a fully-managed service that comes with Snowflake security, configuration, and operational best practices built in.

STREAMLIT IN SNOWFLAKE

Securely build, deploy, and share Streamlit apps on Snowflake’s data cloud.

FUNCTIONS AND PROCEDURES

Enhance and extend Snowflake by writing procedures and user-defined functions. In both cases, you write the logic in one of the supported programming languages.

KAFKA AND SPARK CONNECTORS

Snowflake includes connectors with APIs for integrating with systems outside Snowflake.

DRIVERS

Integrate Snowflake operations into a client app. In addition to the Snowpark API, you can also use language and platform specific drivers.

Drivers allow you to connect from your code or apps to Snowflake. Using languages such as C#, Go, and Python, you can write applications that perform operations on Snowflake.

Using the Snowflake RESTful SQL API, you can access and update data over HTTPS and REST. For example, you can submit SQL statements, create and execute stored procedures, provision users, and so on.

In the SQL REST API, you submit a SQL statement for execution in the body of a POST request. You then check execution status and fetch results with GET requests.

TOOLS

Work with Snowflake using tools that integrate well with your existing workflow.

Use the command line to create, manage, update, and view apps running on Snowflake across workloads.

Execute and use Git repository code directly from Snowflake.

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