Snowflake is one of the most popular choices for building cloud data warehouses, integrating dozens or hundreds of data sources in a single centralized location for easier analysis. But while Snowflake’s record-breaking IPO in 2020 made headlines, it’s not quite as clear how to perform Snowflake data integration.

To use Snowflake as a data warehouse, you'll need some way of connecting Snowflake with your other applications and systems. In this article, we’ll look at 3 methods of Snowflake data integration so you can decide which option is best for your organization.

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Table of Contents

1. Snowflake ETL Tools

ETL (extract, transform, load) is the predominant form of data integration, efficiently and predictably moving your enterprise data into a centralized repository. The good news is that there’s no shortage of Snowflake ETL tools that can help you build an automated data pipeline to your Snowflake data warehouse.

Related Reading:Top 5 ETL to Snowflake Tools for 2021

If you’re connecting Snowflake to the other components of your IT ecosystem, choosing an ETL tool with easy Snowflake integration is a must. But a reliable Snowflake connector is only the start when evaluating Snowflake ETL tools. Other factors to consider include the tools:

  • Selection of connectors and integrations for the data sources in your pipeline.
  • Ease of use, especially for non-technical team members.
  • Pricing model (e.g., flat fee, subscription, per connector, per data volume, etc.).
  • Quality of customer support.

2. Snowflake Connector for Python

If you’re comfortable with software development, you might prefer to build your own connections to your Snowflake data warehouse. Snowflake provides a tool for doing exactly that: the Snowflake Connector for Python.

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You can use the Snowflake Connector for Python to develop Python applications that can connect to your Snowflake data warehouse. This connector enables you to connect to Snowflake, create databases and tables, load and query data, and more—all from within an external Python application. Snowflake’s Python connector is compatible with Linux, macOS, and Windows operating systems and supports Python versions 3.6 and up.

The Snowflake Connector for Python gives you greater flexibility and control over how you integrate your data, but it’s much more technically challenging than a pre-built ETL solution. Prefer to work in a different programming language? Snowflake has you covered as well. Below is a non-exhaustive list of the various software connectors that Snowflake provides:

3. Snowflake Ecosystem

Last but not least, Snowflake provides a list of third-party tools and software that it considers to be part of the “Snowflake Ecosystem.” These technologies have received the Snowflake “seal of approval,” offering guaranteed solutions for connecting to Snowflake.

Working in the Snowflake ecosystem is user-friendly, but restrictive: you can only use applications with pre-existing Snowflake connections. The Snowflake ecosystem has dozens of tools and software, broken into groups such as:

How Xplenty Can Help with Connecting Snowflake

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If you’re looking for the best Snowflake ETL tool, look no further than Xplenty. The Xplenty platform is a powerful, feature-rich, and user-friendly solution for simple, streamlined data integration.

Xplenty has more than 140 pre-built connectors and integrations—including Snowflake. What’s more, Xplenty’s no-code visual interface lets users of any technical skill level drag and drop these connectors to rapidly build a production-ready data pipeline.

Related Reading:Allowing Xplenty Access to My Snowflake Account

Want to learn more about how Xplenty can help connect your Snowflake data warehouse? Get in touch with our team of data integration experts today for a chat about your business needs and objectives, or to start your 7-day pilot of the Xplenty platform.