Snowflake SQLAlchemy Toolkit with Python Connector Simplified 101

Thanks to its automatic administration and maintenance system, Snowflake is fast becoming one of the most used Data Warehouses in the field of Data Analytics. With that said, you’ll need to install the Snowflake SQLAlchemy Toolkit on your Server to help your developers achieve the full potential of the Snowflake Data Warehouse. The Snowflake SQLAlchemy Toolkit contains a variety of high-level persistence patterns which your programmers can utilize to solve arising software problems.

The Snowflake SQLAlchemy Toolkit relies on the Python Connector for Snowflake to function properly. Once you set up your Snowflake SQLAlchemy toolkit, the Python connector will be installed automatically. This connector comes in handy when you need to build Python applications that you can connect to Snowflake. 

Upon a complete walkthrough of this article, you will gain a decent understanding of Snowflake. You will also be able to install and use the Snowflake SQLAlchemy toolkit alongside the Python Connector.

Table of Contents

• Introduction to Snowflake
• Introduction to Python
• Snowflake SQLAlchemy Installation
• Steps to Verify your Snowflake SQLAlchemy Toolkit Setup
• Snowflake Parameters and Behaviors
  • Connection Parameters
  • Opening and Closing a Connection
  • Auto Increment Behavior
• Conclusion

Introduction to Snowflake

Snowflake is a Cloud-based software that allows businesses to Store, Analyze and Process Big Data without worrying about maintenance and administration of their database system. Unlike other database platforms, Snowflake automatically manages and maintains the data of its users. The only thing software developers have to do is install the application, and start coding.  

In addition, Snowflake allows users to Store and Compute data concurrently. Usually, most databases would require you to shut down other database operations in order to load new data into your database system. But Snowflake enables a Multi-Cluster data environment. This means that, with Snowflake, different teams in your organization can perform various operations simultaneously on your database system without affecting one another.

Snowflake also offers convenient pricing for its subscribers. Instead of paying exorbitant prices for your database management, Snowflake only requires you to pay for the amount of data you store and compute. This means that big organizations will pay higher prices for their Snowflake database system than small businesses.

For further information on Snowflake, click here to check out their official website.

Introduction to Python

Python is a Programming Language that enables Software Developers to write readable code for simple and complex operations. This Programming Language is highly favored among programmers because of its vast Libraries, Compact Systems, and tendency to accommodate a variety of frameworks. 

Python has an extensible design format. The Programming Language only contains essential functions on installations. Users who want to enjoy additional functions can access and install them from Python’s libraries. 

So far, Python has been used to build applications in the following industries:

• Gaming, such as Vega Strike and Disney’s Toontown.
• Web development, such as Pyramid and Django.
• Data Science: Python has libraries like NumPy and Pandas, which help data scientists analyze data and extract analytics from the system.
• Social media, such as Instagram, Reddit, Quora, Facebook.
• Entertainment, such as Spotify, YouTube, Netflix.
• Transportation, such as Uber and Lyft.

Snowflake SQLAlchemy Installation

Go to pip on your Python’s Interface, and enter the following code to download the Snowflake SQLAlchemy from the PyPI (Python Package Index) repository:

pip install --upgrade snowflake-sqlalchemy

Meanwhile, using pip to install the Snowflake SQLAlchemy into your system will automatically download the Python Connector for Snowflake. 

Steps to Verify Your Snowflake SQLAlchemy Toolkit Setup

Once you have installed the Snowflake SQL Alchemy and the Python Connector, you’ll need to verify your installation before using the programs.

Follow the steps below to verify your installation:

• Step 1: Create a sample file, for instance, verify.py, on your system. Enter the Python code below in the file to connect to your Snowflake version: 

#!/user/bin/env python
from sqlalchemy import create_engine
 
engine = create_engine(
    'snowflake://{user}:{password}@{account_identifier}/'.format(
        user='<user_login_name>',
        password='<password>',
        account_identifier='<account_identifier>',
    )
)
try:
    connection = engine.connect()
    results = connection.execute('select current_version()').fetchone()
    print(results[0])
finally:
    connection.close()
    engine.dispose()

• Step 2: Edit the entries: <user login name>, <password>, and <account identifier>, and replace them with your Snowflake username, password, and account identifier.
• Step 3: Now execute the Python Sample code by typing the file name in this form: python verify.py. After you execute the code, your system will display your Snowflake version. The version should be 1.48.0 or later.

Snowflake Parameters and Behaviors

The Snowflake Alchemy offers specific Snowflake Parameters and Behaviors to help users perform operations on Snowflake.  Some of the Snowflake parameters and behaviors that its SQLAlchemy provides are explained below:

Connection Parameters

The three essential Snowflake Connection Parameters are:

• <user_login_name>: This parameter refers to the registered name for your Snowflake account.
• <password>: This is where you input the password for your Snowflake account.
• <account_identifier>: This parameter helps you indicate specific details about your account, such as your organization name, account type, or region. 

Snowflake SQLAlchemy displays these Parameters in a string when a user is trying to connect to Snowflake. View the connection string below:

'snowflake://<user_login_name>:<password>@<account_identifier>'

To access your Snowflake account, you must replace each Parameter with your relevant Snowflake information. When entering your Snowflake account identifier into the connection string, avoid including the domain name ‘snowflakecomputing.com’ in your account identifier slot. 

This domain name will automatically appear on the account identifier when the Snowflake SQLAlchemy connects to your Snowflake account.

An example of a Snowflake SQLAlchemy connection string is:

‘snowflake: //analystA:57365@acme-marketingaccount

Opening and Closing a Connection

When opening a connection, always use the engine.connect command. Here’s how to write the code for the engine.connect command:

engine = create_engine(...)
connection = engine.connect()

To close the connection, use the string below:

connection.close()
    engine.dispose()

The right way to close a Snowflake SQLAlchemy is to insert the connection.close() command before entering the engine.dispose function. If you do not follow this order, the Python Garbage Collector will eliminate the resources that facilitate your connection to Snowflake. As a result, the Python connector for Snowflake may be unable to close the connection properly.

Auto Increment Behavior

The Auto-Increment function is only used for numeric databases. This function automatically generates a new value when the user inserts an extra record on the table. Just like with other SQLAlchemy databases, you have to create a primary key field for the command. This is to ensure that your numerical values are automatically incremented when you add a new record. 

Every Auto-Increment command must contain the ‘Sequence’ object.

A typical Auto-Increment command looks like this:

t = Table('mytable', metadata,
    Column('id', Integer, Sequence('id_seq'), primary_key=True),
    Column(...), ...

Conclusion

In this article, you learned about the essential components of the Snowflake SQLAlchemy. You also learned about the steps required to install the Snowflake SQLAlchemy toolkit to enjoy the full capabilities of Snowflake. Manually handling the data regularly across all the applications in your firm can be a tedious task. You would be required to invest a section of your bandwidth to Integrate, Clean, Transform and Load your data into Snowflake for further analysis. All of this can be comfortably automated by a Cloud-based ETL tool like Hevo Data.

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Hevo Data, a No-code Data Pipeline can efficiently transfer your data from a collection of sources into your Data Warehouse like Snowflake or a destination of your choice to be visualized in a BI Tool. It is a reliable, secure, and completely automated service that doesn’t require you to write any code!

If you are using Snowflake as a Data Warehouse in your firm and searching for an alternative to Manual Data Integration, then Hevo can seamlessly automate this for you.  Hevo, with its strong integration with 100+ sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.

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Share your experience of learning about Snowflake SQLAlchemy Toolkit. Tell us in the comments below!