Top 10 Snowflake ETL Tools

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 Snowflake ETL 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.

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. 

Move Data to Snowflake Without Breaking a Sweat

ETL your data to Snowflake and make smarter BI-driven decisions in your workplace. Get started with a 7-day demo. Schedule a Calendly call here. 

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 Snowflake ETL tools are the same.

Let's take an example of Integrate.io's client to understand how Snowflake ETL can be impactful.

Our client was facing issues with tracking and capturing the changes (inserts, updates, and deletes) made to data within their source system particularly with their admin database and JDE data, leading to problems with log clearing, production database strain, and timeout issues.

Integrate.io's features helped to address these pain points, with the ability to load data from various sources like PostgreSQL, SQL Server, Google Sheets, and SFTP into Snowflake with high frequency and reliability.

What are the Top ETL Snowflake Tools?

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

• Integrate.io
• Apache Airflow
• Matillion
• Blendo 
• Stitch
• Airbyte
• StreamSets
• Coalesce
• Estuary Flow
• Fivetran

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

1. Integrate.io

Average user score on G2.com: 4.3/5

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.
• REST API.
• 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. 
• Integrate.io pricing is flat fee, unlimited usage based. 

One of the top Snowflake ETL tools, Integrate.io is an all-in-one ETL solution for Snowflake, boasting a ready-to-use native Snowflake connector. Unlike the other tools on this list, Integrate.io 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 (Integrate.io charges by the connector and not data volume).

Ideal use case: Best for building no-code ETL and reverse ETL pipelines across diverse data sources for operational and analytical use cases.

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 stable version of Apache Airflow is 2.10.5, released on February 10, 202556. Additionally, Apache Airflow 3.0 is in the beta stage, with general availability expected by mid-April 2025.

Ideal use case: Ideal for orchestrating complex workflows and managing scheduled data pipeline dependencies.

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 Integrate.io. 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 December 2024, Gartner named Matillion as a Challenger in its Magic Quadrant for Data Integration Tools for the second consecutive year. 

Ideal use case: Great for transforming data within cloud data warehouses using ELT with an intuitive UI for data teams.

Read More: Integrate.io 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. 

Ideal use case: Suited for syncing SaaS data to data warehouses quickly with minimal setup.

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. 

Ideal use case: Designed for rapid, simple ELT from SaaS apps and databases to data warehouses.

Read More: Integrate.io vs. Stitch 

6. Airbyte

Average user score: 4.5/5

The Good:

• 400+ connectors, including open-source and community-built options

• Flexible deployment: open-source or managed cloud

• Supports custom connector creation using Python or JavaScript

• Incremental sync and CDC (Change Data Capture) for many sources

The Bad:

• Basic transformation capabilities (mainly ELT, not full ETL)

• Requires technical skills for advanced configurations

• Higher memory usage for large-scale jobs

Pricing: Free (open source); Cloud starts at $2.50/credit

Ideal Use Case: Perfect for open-source, customizable data integration with extensive connector support.

7. StreamSets

Average user score: 4/5

The Good:

• No-code pipeline design with AI-assisted mapping

• Built-in Spark engine for scalable processing

• Real-time monitoring and error handling

• Enterprise-grade security (SOC 2 Type II, FedRAMP Moderate)

The Bad:

• Complex licensing and pricing structure

• Steep learning curve for advanced features

Pricing: Custom (starts around $25,000/year)

Ideal Use Case: Best for building and monitoring smart, real-time streaming and batch data pipelines.

8. Coalesce

Average user score: 4.7/5

The Good:

• Snowflake-native transformations and automation

• Visual, column-aware GUI for building and managing data models

• Built-in data quality checks and lineage tracking

• Automated Data Vault 2.0 schema generation

The Bad:

• Only supports Snowflake as a destination

• Smaller user community compared to older tools

Pricing: Contact sales for custom quote

Ideal Use Case: Ideal for modeling and transforming data directly in the warehouse with dbt-like SQL workflows.

9. Estuary Flow

Average user score: 4.8/5

The Good:

• Real-time and batch data integration with sub-100ms latency

• No-code interface for rapid pipeline creation

• Supports ETL, ELT, and CDC workflows

• Handles schema evolution automatically

• Load data into multiple destinations simultaneously

The Bad:

• Advanced features require technical expertise

• Limited customization for highly complex workflows

Pricing: Starts at $0.50/GB

Ideal Use Case: Designed for real-time streaming ETL pipelines with sub-second latency.

10. Fivetran

Average user score: 4.2/5

The Good:

• Fully managed, automated ELT pipelines

• 300+ pre-built connectors for SaaS, databases, and files

• Automatic schema drift handling

• Minimal maintenance required

The Bad:

• Limited transformation capabilities (focuses on ELT)

• Pricing can be high for large data volumes

• Fewer options for custom connectors

Pricing: Monthly Active Rows (MAR) based, starts at ~$100/month

Ideal Use Case: Great for fully managed, automated ELT pipelines from a wide variety of sources to modern data warehouses.

Comparison of Various ETL Tools

How Integrate.io Helps With Snowflake ETL

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

Integrate.io helped the Leukaemia Foundation slash data processing time by 90% by integrating fragmented sources—like Salesforce, Google Analytics, and social platforms—into Snowflake. With a low-code interface and pre-built connectors, the Foundation’s lean data team built robust pipelines in minutes instead of weeks. This centralization enabled real-time analytics, empowered predictive fundraising through machine learning, and ensured scalability during high-traffic campaigns. Backed by responsive, transparent support, Integrate.io became the backbone of the Foundation’s data-driven transformation.

With hundreds of integrations, super-easy data transformations, streamlined workflow creations, a reliable REST API, free support, and loads of other incredible features, the Integrate.io data integration is the only best 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. 

FAQs

Q: Does Snowflake have an ETL tool?

Snowflake itself is not an ETL tool but supports both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes. It integrates seamlessly with various ETL tools like Informatica, Talend, and Matillion to facilitate data transformation and loading into Snowflake. While Snowflake offers some transformation capabilities, it often relies on external tools for more complex ETL operations.

Q: Which tool is used for Snowflake?

Several tools are commonly used with Snowflake for ETL and data integration purposes. Some of the recommended tools include:

• Integrate.io: Specializes in features for transformation 
• Informatica: Known for its robust data integration capabilities.
• Talend: Offers comprehensive data integration and ETL features.
• Matillion: Specializes in cloud-based data integration.
• Etleap: Provides a cloud-native ETL platform.
• Matalin: Another tool compatible with Snowflake for ETL tasks.

Q: What are Snowflake tasks for ETL?

Snowflake tasks related to ETL involve:

• Data Extraction: Pulling data from various sources.
• Data Transformation: Using SQL or external tools to transform data into a suitable format.
• Data Loading: Loading transformed data into Snowflake for analysis.

Snowflake supports these processes through its integration with external ETL tools and its own SQL capabilities for data transformation.

Q: Is Snowflake and Informatica the same?

No, Snowflake and Informatica are not the same. Snowflake is a cloud-based data warehouse platform that supports ETL and ELT processes, while Informatica is a data integration tool that can be used to manage ETL operations for loading data into Snowflake or other data warehouses. Informatica provides advanced data integration capabilities that complement Snowflake's data storage and analytics features.