Unlocking the Power of Data with Snowpark and Snowflake (2024)

Mubashir Masood

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Unlocking the Power of Data with Snowpark and Snowflake (3)

In today’s data-driven world, businesses need to have access to reliable and scalable data platforms to stay competitive. Snowflake is a cloud-based data platform that offers a range of services and tools to help organizations manage and analyze their data effectively. Additionally, Snowflake’s unique architecture allows for seamless data sharing across different organizations and platforms across AWS, GCP and Azure. One of the latest additions to Snowflake’s portfolio of tools is Snowpark, which offers a new way to process data using familiar programming languages. In this article, we will explore how Snowpark can be used to unlock the power of data in Snowflake.

What is Snowpark?

Due to the fact that the Snowpark platform allows developers to use their preferred programming languages in Python, Java, or Scala, this capability enables the users to write queries and extract data from Snowflake without having to pull data out . It provides a high-level interface for developers to work with data, making it easier to create data pipelines and applications that require data processing.

Snowpark is designed to work with a variety of programming languages, including Java, Scala, and Python. This means that developers can use their existing skills and tools to write code that interacts with Snowflake. Snowpark also provides a rich set of APIs for interacting with Snowflake’s data platform, making it easy to work with complex data structures and query results.

Why use Snowpark?

There are several reasons why developers might choose to use Snowpark instead of traditional SQL queries. First, Snowpark offers a higher level of abstraction than SQL, making it easier to work with complex data structures and transform data in ways that would be difficult or impossible using traditional SQL queries. Second, Snowpark allows developers to use their preferred programming languages, which can lead to more efficient and productive coding.

Third, Snowpark provides a more scalable way to process data. Traditional SQL queries executed on other platforms, which usually become a bottleneck as the volume of data, concurrency and complexity increases. Snowpark, on the other hand, allows developers to execute data processing tasks and push down the workload(s) to Snowflake, which can be scaled up or down as needed with the Multi cluster shared data architecture.

Finally, Snowpark provides a more flexible way to process data. With Snowpark, developers can write code that interacts with Snowflake’s data platform in ways that would not be possible using traditional SQL queries. For example, they can use machine learning libraries to perform predictive analytics on Snowflake data, or they can use graph libraries to analyze relationships between different data points.

Using Snowpark with Snowflake

Snowpark can be used in a variety of ways to process and analyze data stored in Snowflake. Some of the key use cases of Snowpark include:

Data transformation: Snowpark can be used to transform data stored in Snowflake using popular programming languages. This can include tasks like data cleaning, data normalization, and data aggregation.
Feature Engineering: Feature engineering is an essential task in data processing and analytics, which involves transforming raw data into meaningful features that can be used in machine learning and data modeling tasks. Feature engineering is crucial in ensuring that machine learning models are accurate and effective in making predictions. Snowpark provides a powerful toolset for feature engineering tasks, making it easy for developers to manipulate and transform data stored in Snowflake.
Machine learning: Snowpark provides a variety of libraries that can be used for machine learning tasks like data modeling and prediction. Developers can use popular machine learning frameworks like TensorFlow and PyTorch to build machine learning models in Snowflake.
Data integration: Snowpark can be used to integrate data from different sources into Snowflake, allowing for a unified view of data across different platforms.
Real-time analytics: Snowpark can be used to perform real-time analytics on data stored in Snowflake using Snowpipe streaming. This can include tasks like streaming data processing, event processing, and near real-time visualization.

How to use Snowpark with Snowflake?

There are several ways to connect Snowpark to Snowflake via an Snowsight or an IDE like VS Code, IntelliJ, and others. You can start using the Python Worksheets (currently this feature is in Public Preview and open to all of our customers). Write Snowpark code in Python worksheets to process data using Snowpark Python in Snowsight. By writing code in Python worksheets, you can perform your development and testing in Snowflake without needing to install dependent libraries.

After logging into Snowflake via Snowsight, go to worksheets and click on the new Python worksheet

Unlocking the Power of Data with Snowpark and Snowflake (4)

Once you have created a new Python notebook, you can check the installed packages and libraries by writing a quick Python script. Start by importing the Snowpark library and defining your session. Then, you can retrieve the table information_schema.packages and store it in a dataframe and show the results. Super easy to use

Unlocking the Power of Data with Snowpark and Snowflake (5)

Regardless of the method you choose, you will need to provide your Snowflake account credentials to establish a connection. Once you have connected to your Snowflake account, you can begin working with your Snowpark data using the IDE of your choice. To use Snowpark with Snowflake, developers need to have access to Snowflake’s cloud-based data platform. Once they have access, they can create a Snowpark session and start writing code to interact with Snowflake data.

Here is a simple example of how to use Snowpark to extract data from Snowflake:

#Import Libraries
from snowflake.snowpark.session import Session
from snowflake.snowpark.functions import udf, sproc, col
from snowflake.snowpark.types import IntegerType, FloatType, StringType, BooleanType, Variant
from snowflake.snowpark import functions as fnimport numpy as np
import pandas as pd
from snowflake.snowpark import version
print(f"Snowflake snowpark version is : {version.VERSION}")
#Connection
from config import snowflake_conn_prop
session = Session.builder.configs(snowflake_conn_prop).create()
print(session.sql('use role ACCOUNTADMIN').collect())
print(session.sql('use IMDB.PUBLIC').collect())
print(session.sql('select current_role(), current_warehouse(), current_database(), current_schema()').collect())
#Connecting to table using a snowpark data frame
val df = session.sql("SELECT * FROM mytable")
df.show()

In this example, we first import the necessary libraries for Snowpark. Then we create a Snowpark session using a configuration file that contains the connection details for our Snowflake account. Finally, we execute a SQL query to extract data from a table in Snowflake, and we display the result using the show() method.

Conclusion:

Snowpark is a powerful new tool from Snowflake that allows developers to unlock the power of data using familiar programming languages. By providing a high-level interface for working with data, Snowpark makes it easier to create data pipelines and applications that require data processing. With Snowpark, developers can use their existing skills and tools to work with Snowflake’s data platform, making it easier and more efficient to process data at scale. If you’re looking to take your data processing capabilities to the next level, Snowpark is definitely worth exploring.