Data is a valuable asset for every organization. Over the years, as data grew, so did Data Managing and Storing complexity. Another problem was to decide which is the right Data Platform to handle large volumes of data, reliable, and most importantly, user-friendly. Although most organizations now use Cloud Data Platforms, a few are still considering if a Data Migration is necessary to remain competitive. One of the most popular and reliable Data platforms is Snowflake, which can handle Multi-cloud architecture environments. Snowflake along with Pandas (A Python Library) and a robust ecosystem of open-source libraries, lets users discover and handle data wherever it exists, allowing them to accelerate their pace of innovation.

In this article, we are about to learn about steps to install and leverage the Snowflake Pandas Connector.

Prerequisites

  • Basic understanding of Cloud Computing.

What is Snowflake?

Snowflake Pandas connection -Snowflake Logo
Image Source: Snowflake

In 2012, the Snowflake founders realized there were significant issues while scaling large data solutions for which they collaborated to form a Data Cloud Architecture, which can enable thousands of organizations to explore, share, and unleash the actual value of their data in real-time. Benoit, Thierry, and Marcin, the Snowflake creators, were the first to rethink and rearchitect Cloud-based technology entirely.

Snowflake’s quick customer acceptance and company success were fuelled by this innovative data architecture’s substantial differentiation in speed and scale, ease of use, and data sharing. Snowflake does not comprise any hardware or software to select, install, configure, or manage. Hence, it’s ideal for organizations that don’t want to dedicate resources for setup, maintenance, and support of in-house servers.

A) Key Features

  • Snowflake’s single Elastic-performance Engine provides near-unlimited scaling in an instant.
  • The multi-cluster resource separation that Snowflake provides allows users to support a virtually unlimited number of concurrent users and workloads spanning from interactive to batch, all with dependable, fast performance.
  • Snowflake allows for seamless Cross-cloud and Cross-region connectivity and global governance regulations that follow the data.

B) Snowflake Data Platform as a Cloud Service

All the components of Snowflake’s service run entirely on the public Cloud Infrastructures. Snowflake’s Compute demands are met by virtual Compute instances, and data is stored permanently through a storage service.

C) Architecture

The architecture of Snowflake is a hybrid of traditional Shared-disk and Shared-nothing Database Architectures.

  • Shared-disk Architecture: Snowflake employs a central data repository available from all the Compute nodes in the platform for persistent data.
  • Shared-nothing Architecture: Snowflake uses MPP (massively parallel processing) Compute clusters to perform queries, with each node storing a fraction of the complete data set locally.

This hybrid approach offers the ease of data management of a shared-disk architecture with the speed and scale-out advantages of a shared-nothing architecture.

Snowflake Pandas connection - Snowflake Architecture
Image Source: Snowflake
  • Database Storage: Whenever the data is imported into the Snowflake, it reorganizes the data into a columnar format that is internally optimized and compressed and then saves the data on the Cloud. In other words, Snowflake manages all data storage elements, including organization, file size, structure, compression, metadata, and statistics.
  • Query Processing: The queries are executed in the processing layer. To process queries, Snowflake uses “virtual warehouses.” Each of these warehouses is an MPP Compute Cluster made up of many Compute nodes that Snowflake obtains from a Cloud provider.
  • Cloud Services: This layer is a set of services that help Snowflake coordinate its actions. These services connect Snowflake’s various components in order to process user requests, from login through query dispatch. This layer manages the following services: Authentication, Infrastructure administration, Metadata management, Query optimization, and Access control.

What is the Snowflake Connector for Python?

The Snowflake Connector provides a Python interface to perform all the standard operations and a programming alternative to develop applications in C/C++ or Java using the Snowflake ODBC or JDBC drivers. The connector is a pure Python package with no dependencies on ODBC or JDBC. It can be installed using pip on operating systems where a supported version of Python is already installed.

Steps to Install the Snowflake Python Connector

Here are the steps you can follow to get started with the installation process for your Snowflake Python Connector:

A) What are the Technical Requirements?

  • Operating System: Linux  – Ubuntu 16.04, 18.04 or CentOS 7, 8 or Red Hat Enterprise Linux (RHEL) 7, 8 macOS – 10.14 or later, Microsoft Windows – 8 or 8.1 or 10.
  • Python version 3.6 or later.
  • pip version 19.0 or later

B) Installing the Connector

Step 1: To install dependent libraries, type or paste the following command. 

pip install -r https://raw.githubusercontent.com/snowflakedb/snowflake-connector-python/v2.7.3/tested_requirements/requirements_36.reqs

Step 2: To install the Snowflake connector, run the following command.

pip install snowflake-connector-python==2.7.3

C) Verifying the Installation

Step 1: Create a file with the name validate.py and copy the following Python code in it.

Snowflake Pandas connection - Verifying Snowflake Installation
Image Source: Snowflake

Replace <account_identifier>, <user_name> and <password> with your username,password, and account-identifier, respectively.

D) Specifying the Temporary Directory

The Snowflake Connector for Python utilizes a temporary directory to store the data for loading and unloading (PUT, GET). The temporary directory can be specified by setting the TEMP, TMPDIR, or TMP environment variables. The default temporary directory of the operating system (i.e., /tmp, C:temp) is usually utilized. If the system’s directory volume is insufficient to analyze the data, use any of the available environment variables to designate an alternative directory with the following command:

export TMPDIR=/large_tmp_volume
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What is Pandas?

Pandas is an open-source Python Data Analysis and Manipulation Library. It is used to analyze and modify two-dimensional data, and it utilizes a data structure called a DataFrame. Along with Data Analysis and Manipulation, Pandas also offers Data Cleansing, Data Exploration, and Visualization tools.

Snowflake Pandas connection - Pandas Logo
Image Source: Pandas

Installing Pandas

Step 1: Install Python and pip before installing Pandas in the system.

Step 2: Type/Paste the following command in the command prompt/console:

  pip install pandas

In case the above command fails: 

Step A: Download Python Ecosystems on the device, such as Anaconda and Spyder, where Pandas is already installed. 

Step B: In the Anaconda prompt, type “jupyter lab” and hit enter to start the Jupyter lab.

Step C: Now, in JupyterLab, create a new Python 3 notebook.

Importing Pandas

Example:

import pandas as pd
file_name = pd.read_csv("csv_name.csv")
display(file_name)

It will display the CSV in tabular format.

Steps to Configure Snowflake Pandas Connection

What are the Technical Requirements?

  • Pandas 0.25.2 or higher.
  • Pip 19.0 or higher.
  • The supported version of Python.

How to Install?

Run the following command to install the Snowflake Pandas -compatible Connector for Python.

pip install "snowflake-connector-python[pandas]"

Note:

  • The square brackets specify the extra elements of the package that should be installed.
  • Use quotes around the package name to avoid the square brackets being misinterpreted as a wildcard.

Using Snowflake Pandas Connector to Read Data 

Use one of the following Cursor methods to read data into a Pandas DataFrame:

fetch_pandas_all()
OR 
fetch_pandas_batches()

Using Snowflake Pandas Connector to Write Data 

Use one of the following methods to write data into a Pandas DataFrame:

  • Call the write_pandas() function.
  • Call the pandas.DataFrame.to_sql() method, and specify pd_writer() as the method to insert the data into the database.

Mapping Data in Snowflake Pandas Connector

This is how you need to map your Snowflake Data Types while using the Snowflake Pandas Connector:

FIXED NUMERIC type (scale = 0) except DECIMAL → (u)int{8,16,32,64} or float64 (for NULL)
FIXED NUMERIC type DECIMAL  → decimal
FLOAT/DOUBLE  → float64
VARCHAR  → str
BINARY → str
VARIANT  → str
DATE  → object (with datetime.date objects)
TIME  → pandas.Timestamp(np.datetime64[ns])

Note: In case the Snowflake data type is ‘FIXED NUMERIC,’ scale is ‘zero,’ and value is ‘NULL,’ then the value is not converted to an integer type but to float64. If any of the Snowflake to Pandas Data Type Conversion causes overflow, the Python connector will immediately throw an exception.

Advantages of using the Snowflake Pandas Connector

  • Users can operate with increased trust and security by removing ungoverned data and executing all code in a highly secure sandbox directly inside Snowflake.
  • Python’s familiar syntax and a robust ecosystem of open-source libraries can let them discover and handle data wherever it exists, allowing them to accelerate their pace of innovation.

Conclusion

Snowflake offers every organization to deploy their data with the help of the Snowflake Data Cloud. Even customers utilize the Data Cloud to bring disparate data sets together, find and securely exchange data, and run various analytic workloads. Snowflake provides a consistent data experience across various Clouds and locations, regardless of where data or users are located. Today, Snowflake is used by many organizations, including 212 of the 2021 Fortune 500, to power their businesses.

In this article, you learned a simple method to install and connect the Snowflake Connector with Python as well as the Snowflake Pandas setup.

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