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?
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.
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.
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
Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up Data Integration for 100+ Data Sources (Including 40+ Free sources) and will let you directly load data to a Data Warehouse such as Snowflake or the Destination of your choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have Analysis-ready data.
Get Started with Hevo for Free
Let’s look at some of the salient features of Hevo:
- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for Analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms such as WordPress, FTP/SFTP, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks; MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-Day Free Trial!
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.
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.
Hevo Data, a No-code Data Pipeline can seamlessly transfer data from a vast sea of 100+ sources to a Data Warehouse such as Snowflake or a Destination of your choice to be visualized in a BI Tool. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!
Visit our Website to Explore Hevo
If you are using Snowflake as your Cloud Data Warehousing & Analytics Solution and searching for a no-fuss alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources & BI tools (Including 40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it Analysis-ready in a jiffy.
Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan fulfills all your business needs.
Tell us about your experience of setting up the Snowflake Pandas Connection! Share your thoughts with us in the comments section below.