Getting Started with Snowpark

How can you decide what action is best for your company? Intuition is helpful when making decisions, but you should really rely on data — all the qualitative and quantitative details recorded in business apps and spreadsheets.

Now, each data specialist uses their preferred language, tools, platforms, etc. to work on this data. These increase the underlying architecture’s complexity and raise the cost as well. It produces data silos, a common cause of friction in businesses that hampers the capacity of your firm to run smoothly and exposes your data to security problems, in addition, to duplicate entries, outdated data, and human errors.

What if there was a way to allow everyone on your team to collaborate safely on the same data on a single platform that supported all languages?

The solution is Snowpark, the Snowflake developer framework that provides Data Programmability for All Users Regardless of Coding Languages.

The Snowpark library offers a simple API for processing and querying data pipelines. By utilizing this library, you can develop applications that process data using Snowflake without transferring it to the machine that is running the program’s code.

Without the requirement for a separate processing system, Snowpark operates right inside Snowflake and lets users write code using their familiar conventions.

Using your preferred external libraries and custom logic, you can create your own user-defined functions with Snowpark. For instance, you could create functions for data augmentation, transformation, ML scoring, and business logic. The functions would then be automatically pushed to the Snowflake engine, which would handle the work.

Everything takes place on a single, simple-to-use platform with excellent performance and almost little maintenance.

Additionally, Snowpark prioritizes security and governance control by securing your data from external attacks and internal errors.

Your data and code reside in a single, highly secure system under full admin control so that you can keep track of exactly what is happening in your environment rather than traveling between other platforms.

Consequently, data will receive more attention than infrastructure management.

Snowpark’s capabilities

The following capabilities are combined into one comprehensive solution by Snowflake’s Snowpark. Utilizing Snowpark to access your Snowflake data is just simpler, more efficient, and more successful.

A model that abstracts the database and permits the use of all preferred languages, environments, and frameworks are offered by Snowflake’s Snowpark. It is intended to make maintenance easier by enabling a more straightforward yet intelligent environment with increased user access to the data they require, increasing efficiency in delivery.

It makes it simpler for developers by allowing them to work on data using familiar code tools and languages. The Snowflake Data Cloud’s programmability has been revolutionized by the Snowpark API. This makes it simpler for data scientists, data engineers, and application developers to cooperate and streamline their data architecture by putting them all on the same platform.

When compared to having a single dedicated server, the ability to scale Snowflake up and down on demand allows us to save money on any compute that is not being used during the day.

Now let us discuss these in detail:

• No Language barrier. Customers may simply move their business logic thanks to the flexibility to leverage existing code bases and the capability of Java, Scala, and Python. No matter what language you code in, Snowpark API converts it into SnowSQL behind the scenes and shoots that query into the Snowflake virtual warehouse.

• Easy to use API. The potential of interacting with the widely used DataFrame API will also make it substantially simpler to retrieve data programmatically.
• System for reliable data intake and integration. Users can incorporate different data types, performance metrics, and calculations.
• A standardized approach to data engineering. The ability to test data pipelines makes genuine CI/CD and unit testing possible. Data pipelines are simpler to comprehend and analyze.
• Access to libraries from third parties. Incorporates data science and machine learning processing.
• Machine Learning. Data Scientists no more need to write verbose SQL queries.

Let us now see how we can work with Snowpark using Python.
We will do this using a Jupyter notebook.

1. Connect to Snowflake

You may quickly establish a connection between Snowflake and your local notebook instance using the Snowpark API.

• Configure the Snowflake connection settings.

• Creating a session

2. Create Dataframes for Snowflake Tables

We can instantly display the data in any Snowflake table using Snowpark’s database abstraction and integrated Pandas support.

3. Using SQL

4. Create UDFs

5. Create stored procedures

6. Store as view

7. Store as a table

You get access to hundreds of well-selected open-source Python packages with Snowpark for Python. As the Conda package manager is fully integrated, you don’t need to perform any manual installations or handle any dependencies in order to use these packages in Snowflake.

Advantages of Snowpark with Python

The goal of Snowpark is to make it simple for you to use data in incredibly powerful ways while maintaining the platform’s simplicity, scalability, and security.

The benefits of using snowpark with Python are as follows:

1. Python Familiar Syntax
2. Secure Access to Anaconda Open-Source Libraries
3. User-Defined Functions — In Snowflake virtual warehouses, Python code can be completely pushed down to execute as a UDF in a very secure sandbox.
4. Performance and Scalability — The virtual warehouses at Snowflake receive all transformations, which in turn provide excellent performance and scalability.
5. Use Rich Machine Learning Tools Ecosystem — Connect to software such as AWS Sagemaker, Azure ML, Dataiku, DataRobot, H2O.ai, Jupyter, etc.
6. Enrich your Data with Third-party Datasets — To get rapid access to data that will enhance your features, use the Snowflake Data Marketplace.
7. Data as one Hyper-parameter — The Snowflake Zero-Copy Clone feature gives users the option to instantly duplicate any user data. Data scientists can always go back and find the data that was used for training, making this an extremely potent tool.