Reports and records. Sales sheets and spreadsheets. Files and financials. Your team has more big data than you can comprehend spread across multiple data sources in more locations than a James Bond movie. Isn't it time you kept this data somewhere safe?  

Moving data to a data warehouse like Snowflake is like keeping thousands of books in a library or a trove of treasure in an underground vault. Big data, your most prized asset, will be safe and snug. Also, use Snowflake to store data for real-time big data analytics, which generates top-secret intelligence for business growth. 

In this guide, learn the top ETL Snowflake tools for the year ahead and why using one of these tools improves data integration in your organization.

To learn more about our Snowflake native connector, visit our Integrations page.

Table of Contents

  1. What is Snowflake?
  2. What is ETL for Snowflake?
  3. Why ETL for Snowflake?
  4. What are the Top ETL Snowflake Tools?
  5. How Helps With Snowflake ETL

What is Snowflake?

Snowflake is a sturdy and super-scalable cloud data warehouse designed to support your business intelligence (BI) needs. It's available as Software-as-a-Service (SaaS). Data-driven teams like yours need a warehouse such as Snowflake to make time-critical decisions that power business growth. 

Snowflake incorporates and centralizes massive amounts of data (structured, semi-structured, JSON, XML, etc) from multiple data sources so you can analyze it all with greater clarity and precision. Sounds good. 

But analytics is the easy bit. Getting data to Snowflake in the first place can be a head-scratching challenge. 

The great thing about Snowflake is that it’s ever-evolving, with new features added to the platform all the time. In September 2021, Snowflake introduced a wealth of new features including:

  • The ability to schedule the execution of SQL statements with serverless tasks. Now Snowflake leverages serverless computing and machine learning to manage warehouse size and idle policy based on customers' pipelines. 
  • Snowflake is now available in more regions than ever before. These regions include US Gov West 1 and Asia Pacific (Seoul) for AWS users and Northern Europe (Ireland) for Microsoft Azure customers.  
  • Users can also access third-party services and data from over 200 providers on the Snowflake Data Marketplace and market their own products on the Snowflake Data Cloud. 


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What is ETL for Snowflake?

ETL stands for Extract, Transform, Load — three critical words when moving data to Snowflake. Here's how it works:

  • ETL extracts data from all kinds of data sources. Think relational databases, flat files, legacy systems, SaaS sources, CRMs, ERPs, etc. 
  • It transforms the data from an unusable format to a usable format. (Otherwise, the data is useless.)
  • It loads the data into a warehouse like Snowflake. 
  • Your data is now ready for analytics!

This entire process can take mere minutes when you use an ETL tool for Snowflake. Otherwise, manual ETL requires complex code and pipeline-building that can take up resources and labor. 

Why ETL for Snowflake?

ETL tames data and gives it a much-needed makeover so it's ready for Snowflake and then ready for analytics. You can improve source data, data sets, data quality, and more.

But why is ETL necessary? 

Many data warehouses and BI tools can't handle enormous amounts of data from a bundle of different sources. Some platforms have to standardize data formats and join up records, grinding analysis to a halt. When you extract, transform, and load data, none of this is a problem.

The ETL process preps enormous data volumes for Snowflake storage, combining structured and unstructured data from even the most obscure data sources. (Those weblogs from the '90s? Not a problem.) This reduces time and hassle. 

Here are some reasons to ETL data to Snowflake:

Visualize Data

You want a visual representation of all the data in your organization. Say you move data from a CRM system to Snowflake via ETL and then run that data through a BI tool like Looker. You can generate data visualizations that provide granular data about customer outcomes. Sales and marketing teams can use these visualizations to fine-tune campaigns and move more customers through their funnels. 

Data Cleansing

Data that exists in legacy systems might be out of data, inaccurate, or potentially violate data governance frameworks like GDPR. That’s why it’s important to cleanse that data before it gets to Snowflake so it’s more consistent and reliable. ETL tools can cleanse your data without human intervention. 

One Single Source of Truth

Instead of keeping data in multiple systems, you can centralize data management by just using Snowflake. This data warehouse can become a ‘single source of truth’ for all data in your organization. 

Of course, not all top ETL Snowflake tools are the same.

What are the Top ETL Snowflake Tools?

We've compiled five of the best ETL for the Snowflake data warehouse, so you don't have to. These tools are:

  • Apache Airflow
  • Matillion
  • Blendo 
  • Stitch

Also, check out Top Python ETL Tools, The Best ETL Tools for MySQL, and How to Pick an ETL Tool.


Average user score on 4.3

The Good:

  • Native Snowflake connector.
  • No code.
  • Simple drag-and-drop interface.
  • A super-generous 200+ data sources (more than any other on this list).
  • Free customer support for all users.
  • Easy data transformations and data flows regardless of schema.
  • Charges by the connector, not data volume, which could work out cheaper.
  • Integrations with many other cloud platforms, databases, systems, apps, and data warehouses including AWS, Microsoft Azure, Redshift, Talend, Oracle, Microsoft, Tableau, and Salesforce.
  • Schedule ETL jobs on your terms, with the ability to run data processes whenever you like.
  • Data integration automation.
  • Enhanced data security and compliance. Xplenty transforms your data before it gets to Snowflake so you can comply with GDPR, HIPAA, CCPA, and other data governance frameworks. Adhering with these frameworks can help you avoid expensive penalties for non-compliance. 

One of the top ETL Snowflake tools, is an all-in-one ETL solution for Snowflake, boasting a ready-to-use native Snowflake connector. Unlike the other tools on this list, requires no code, making it a good fit for teams of all sizes. There's free customer support for all users, more than 200 data sources, simple data transformations, a drag-and-drop interface, and a simplified pricing structure ( charges by the connector and not data volume).

Integrate Your Data into Snowflake Today

ETL data from various sources into Snowflake and generate deeper data insights that power your team. Schedule a demo via Calendly here and optimize data warehousing.

2. Apache Airflow

Average user score: 4.3/5

The Good:

  • Extensive support via Slack
  • Scalable pricing 
  • Excellent functionality 

The Bad:

  • Moves data from sources via plugins
  • Python only

Apache Airflow is an open-source project that facilitates ETL for Snowflake. It's one of the most popular ETL tools on the market. Unlike other data platforms on this list, Airflow moves data from sources via plugins — essentially templates written in Python. So if you don't know Python, you're going to struggle to extract, transform, and load data into Snowflake. On the plus side, there's extensive support via Slack and scalable pricing, where smaller teams with fewer ETL requirements pay lower costs than larger teams. 

The most recent version of Apache Airflow, released in October 2021, offers custom timetables, deferrable tasks, and validation of DAG params. 

3. Matillion 

Average user score: 4.4/5

The Good:

  • 70 data sources
  • Annual billing plans available

The Bad:

  • Requires knowledge of SQL
  • Limited click-and-point 
  • No training videos

Matillion is a cloud-based ETL platform that moves data from 70 data sources to Snowflake. But click-and-point capabilities are weak compared to low-code alternatives While users can drag components onto visual workspaces at a specific point in a pipeline, the entire process requires SQL knowledge. Still, Matillion's data sources include a broad range of databases, social networks, CRMs, and ERPS, and users can create additional data pipelines if needed. Matillion ETL charges by the hour, and there are annual plans available. 

In 2021, Gartner named Matillion in its Magic Quandrant for Data Integration Tools, a popular annual market research report that evaluates vendors.

Read More: vs. Matillion 

4. Blendo

Average user score: 5/5 

The Good:

  • 50 data sources to choose from

The Bad:

  • It doesn't transform data
  • Users can't build additional data sources
  • No training 

Blendo is an ELT (not ETL) platform that moves data to Snowflake successfully. However, it focuses on extracting/loading and doesn't transform data from sources. This is problematic if organizations need to transform data before loading it to a warehouse, especially when adhering to data safety and compliance requirements. Users can request Blendo to create data sources that serve their needs but can't build sources themselves. Still, there are 50 data sources to choose from, including popular CRM and ERP systems. 

Other Blendo features include data analysis, master data management, data filtration, and API integration. 

5. Stitch

Average user score: 4.7/5 

The Good: 

  • 100 database and SaaS integrations
  • Users can add additional data sources via open-source Singer

The Bad:

  • Users need to know code

Stitch is a cloud-based ELT (not ETL) solution used by thousands of companies. Again, the ELT approach might not be suitable for organizations concerned about data compliance. It's a suitable choice for larger teams, boasting more than 100 integrations. This popular platform makes it easy to move new data to the Snowflake database — as long as you know Python, SQL, or another programming language! (Not all teams have these skills.) 

Other Stitch features include APIs, reporting, and data extraction. 

Read More: vs. Stitch 

How Helps With Snowflake ETL

These top ETL Snowflake tools pack an almighty punch, but wins the knockout. With its native Snowflake connector and no-code and point-and-click interface, benefits organizations that lack a data engineering team, seamlessly extracting, transforming, and loading data to Snowflake like a heavyweight champion. 

With hundreds of integrations, super-easy data transformations, streamlined workflow creations, a reliable REST API, free support, and loads of other incredible features, the data integration is the only ETL tool for Snowflake you need.

Do you want to move data to Snowflake without breaking a sweat? Schedule a 14-day demo with our support team