Snowflake Connectors 101: How To Sync Multiple Data Sources

CEO, Portable

Snowflake Overview

Snowflake data warehouse is a cloud-based data warehousing solution. It allows you to store, analyze, and share your data securely. It is designed for businesses of all sizes and industries.

Snowflake is a SaaS solution. Therefore, you don't have to do any hardware or software configurations to use it.

Snowflake can be accessed from anywhere in the cloud. As a result, this data warehousing platform is also beneficial for companies that operate globally.

Reasons to use Snowflake

  • Snowflake can handle gigantic volumes of data. This benefit provides the scalability and performance needed for advanced analytics processes.

  • Snowflake's documentation features a wide range of data sources. This advantage helps data engineers to ingest and integrate different data types into their analytics processes. These data sources, such as JSON, XML, or Avro, can be structured or semi-structured.

  • Snowflake provides a flexible schema that makes it easy for data engineers to manage and query their data. They can easily modify the schema to accommodate changes in the data over time.

  • Snowflake provides robust security features to protect data against unauthorized access. They support features like data encryption, access controls, and user authentication.

  • Snowflake separates its computing resources from its storage. Therefore, Snowflake pricing is fairer than others. You don't have to pay for features you don't use.

  • Snowflake's architecture allows data engineers to scale their analytics processes instantly, making it a common tool found in the modern data stack.

Snowflake's architecture

There are three main constructs to Snowflake's architecture. In a nutshell, they are:

1. Limitless database storage

Snowflake achieves limitless database storage through the use of compressed data sets. Snowflake automatically compresses data and uses columnar storage to minimize storage requirements. It also supports a variety of data types to suit different use cases.

2. Scalable query processing

Scalable query processing is achieved using SQL queries and computing resources. Snowflake automatically partitions data and parallelizes queries across clusters of computing resources. This feature allows for high-performance query processing and efficient use of resources.

3. Accessible cloud services

Accessible cloud services are provided through several features: OAuth, authentication, metadata management, access control, and APIs. In addition, Snowflake offers a variety of options for accessing and managing data. Some examples are web-based tools, command-line interfaces, and programming interfaces. As a result, users can choose a tool that best fits their needs.

Popular Snowflake use cases

  • Providing a single source of truth across multiple data sources. This approach helps businesses to make better-informed decisions by gaining a complete view of their data.

  • Managing marketing data, including customer behavior and trends. This helps businesses better understand their customers and develop effective marketing strategies.

  • Analyzing transactional data, including financial transactions and purchase histories. This function helps businesses better understand their revenue streams and optimize their financial performance.

  • Operationalizing data lakes for storing and analyzing large amounts of data. This core feature maintains timestamps and is available for future data insights.

  • Supporting real-time analysis of multiple data types. It provides businesses with up-to-date insights that help them make faster and more accurate decisions.

  • Allowing for unrestricted big data exploration. This freedom enables enterprise data teams to perform ad-hoc analysis and data discovery.

  • Automating and scaling data collaboration. It allows teams to collaborate on data projects and share insights more quickly. Its data connectors keep it flowing.

Snowflake components

Snowflake has several components that work together, making it a robust data warehousing solution. These components include:

  • Public cloud infrastructure

  • Metadata

  • Third-party data sources

  • Snowflake clients

Each component enables businesses to manage and analyze their data effectively.

Public cloud infrastructure

You can't install Snowflake locally or run it on a private cloud. Instead, Snowflake itself manages all installations and updates.

Snowflake runs on the widely accepted public cloud infrastructure. This cloud architecture allows businesses to scale up or down as needed.

Currently. it supports the three leading public cloud service providers. Those are Amazon AWS, Microsoft Azure, and Google Cloud Platform.

Each platform provides one or more regions where Snowflake accounts can be provisioned.

Snowflake metadata

The metadata includes information about the data schema. Metadata describes the structure of the data and how it is organized.

Snowflake uses metadata to manage and organize data within the data warehouse. This helps businesses to collect and analyze their database changes over time.

Third-party data sources

Snowflake supports a wide range of third-party data sources. This includes data pipelines, ETL tools, and other data integration solutions. This data source management makes it easy to ingest and integrate data from different sources into Snowflake.

Snowflake also provides a Snowflake Connection service. This API enables businesses to connect to other Snowflake accounts and access data securely.

Snowflake clients

Snowflake provides a range of clients and tools to access and manage their Snowflake accounts. Some examples are

  • SnowSQL CLI or the command line client

  • Snowflake Extension for Visual Studio Code

  • Snowsight for users to manage their Snowflake account

  • Snowflake Database as a read-only database with metadata and historical usage data.

These clients make it easy for businesses to access and work with their data, regardless of their preferred access method.

Different types of Snowflake connectors

1) Portable

Portable is a Snowflake ETL tool with more than 300 data connectors. You can use them to connect to various data sources and data warehouses, including Snowflake. These data connectors include even long-tail data sources that are hard to find.

Portable has connectors for CRM platforms, marketing analytics, and e-commerce systems. It also allows for unlimited data volume and provides data integration capabilities.

2) Spark

This connector allows for bi-directional data movement between Snowflake and Apache Spark. In addition, it enables Spark to read and write data from Snowflake, making Snowflake a part of the Spark ecosystem.

3) Apache Kafka

The Snowflake Connector for Kafka efficiently streams data from Apache Kafka to the Snowflake Data Cloud. In addition, the Spark connector allows for real-time data integration. This is ideal for businesses with time-sensitive data requirements.

4) Python

Snowflake has a connector for Python. It allows for easy integration between Python applications and the Snowflake Data Cloud. Many data engineers like using this data connector as a straightforward way to pipe data to Snowflake's data warehouse. This is an ideal choice for developers and data scientists who prefer Python.

5) Node.js

The Snowflake Node.js Connector is a popular tool for connecting Node.js applications to the Snowflake Data Cloud.

The connector is designed to be easy to use and offers many features. Some examples are parallel data ingestion and optimized bulk loading.

The connector supports many standard Node.js libraries. Some examples are Express, Koa, and Hapi. Additionally, this connector is regularly updated with new features and improvements.

6) Snowflake JDBC Driver

The Snowflake JDBC Driver is a Java library for connecting to Snowflake. It allows Java applications to communicate with Snowflake, a cloud data warehouse. The driver supports all Snowflake-specific SQL commands and provides high performance and reliability. With this connector, Java developers can easily integrate Snowflake into their applications.

7) Snowflake ODBC Drivers

Snowflake ODBC drivers are available for various platforms, including Mac OS and Linux. These drivers help users can connect to Snowflake from their preferred ODBC-enabled applications.

For example, there is a Snowflake ODBC driver for SAP. It is optimized for accessing data stored in Snowflake from SAP systems.

Snowflake ODBC drivers provide high-performance connectivity to Snowflake. This enables efficient data access and manipulation.


The Snowflake REST API allows businesses to access the Snowflake Data Cloud programmatically. Snowflake's documentation provides a set of REST endpoints that enable developers to automate various tasks.

Examples of its functions include data integration, data warehousing jobs, and automated data analysis.

9) Oracle

The Oracle Built-In Snowflake Connector helps businesses integrate their Oracle Analytics with Snowflake. This tool moves data between the two platforms. This makes it simpler to analyze and gain insights from large datasets.

One advantage is real-time data processing, providing users with the most up-to-date data. Plus, the connector is simple to set up and use, with a quick webinar tutorial available for those who need assistance.

10) Salesforce connector

The Snowflake Data Connector for Salesforce integrates Salesforce data with Snowflake. For example, you can use it for real-time analysis of Salesforce commerce data.

This connector provides a scalable storage solution for Salesforce data. Moreover, it enables the creation of insightful reports using the Snowflake schema.

The data integration uses Snowflake account authentication details for secure integration.

How to optimize Snowflake performance

Optimizing Snowflake's performance ensures that data is processed efficiently and effectively. This also helps organizations to reduce costs and improve the overall quality of their data processing. This is especially important when dealing with large datasets or real-time data processing. Let's see some Snowflake ETL best practices.

However, it's recommended to get the assistance of Snowflake consulting firm if you don't know how to implement these best practices.

Stage your datasets

One way to optimize Snowflake's performance is by staging datasets. To do that, you must create a temporary area to transform the data. Then you can clean it before being loaded into the target tables.

Separating the concerns of data extraction, transformation, and loading, reduce complexity. It also makes it easier to troubleshoot any issues that may arise during the ETL process. Additionally, this provides a way to handle any data that may fail validation without impacting the data in the target tables.

Leverage the COPY command

The COPY command is one of the most important features of Snowflake for data loading. It leverages Snowflake's parallel processing capabilities for fast and efficient data ingestion. By using the COPY command, organizations can significantly reduce the time it takes to load data into their target tables.

This function can improve overall performance, reduce processing costs, and ensure the reliability of the ETL process.

Process one row at a time 

Processing one row at a time is a critical best practice for Snowflake performance. By performing fast lookups on each row, Snowflake can retrieve data from the target tables more quickly.

This optimization improves overall performance. This is especially important when dealing with large datasets.

Use appropriate data types

Using appropriate data types is crucial for optimizing Snowflake's performance. Selecting the right data types can reduce the amount of storage required for their data. This factors into lower costs and better overall performance. Additionally, this ensures that Snowflake can process the data as quickly as possible.

Send as little data as possible

Sending as little data as possible is an essential best practice for Snowflake performance. It reduces processing times and improves overall performance. This is especially important when dealing with large datasets or real-time data processing. Processing times can significantly impact the reliability of the ETL process.

Take advantage of Snowpipe API

Using the Snowpipe API is another best practice for Snowflake performance. Snowpipe automatically loads data from an external stage into a Snowflake table as soon as it's available.

Organizations can automate their ETL process by using the Snowpipe API. This feature reduces the time and resources required for data ingestion.

Clone Snowflake tables

Cloning Snowflake tables is a best practice for data management and performance optimization. It enables organizations to create a copy of an existing database, table, or query result, with the option to include or exclude certain data.

Duplicating Snowflake tables is handle for testing, backup, analytics, performance, and data governance. Moreover, it ensures that data is easily accessible and available for analysis.

Try Portable for long-tail ETL connectors into Snowflake

Portable is one of the best ETL tools for connecting long-tail data sources to Snowflake easily.

With Portable, users can seamlessly integrate numerous data sources. It offers connectors for CRM platforms, marketing analytics, e-commerce systems, and more. You can use them to push data into Snowflake for analysis and insights.

Portable also offers connectors for many popular data warehouses. Some examples are Snowflake, Amazon Redshift, Google BigQuery, and PostgreSQL. This allows users to move data easily between different platforms as needed.

Portable has over 300 ready-to-use data sources. The company can also develop custom connectors quickly and easily upon request.

Plus, with unlimited data volume and robust ETL capabilities, Portable is the perfect solution for companies of any size looking to streamline their data integration workflows and gain new insights into their business.