9 Best Snowflake ETL Tools: Pros, Cons, Features (August 2024)

Snowflake is one of the most innovative cloud data warehouses for managing big data. Its novel approach to simplifying complex data sets atop Google Cloud Platform, Amazon Web Services, or Microsoft Azure cloud architecture

Data analytics teams often need to modify data with a data pipeline. Snowflake offers a high degree of flexibility for Extract, Transform, and Load (ETL) operations.

As such, many Snowflake ETL tools offer unique approaches to data integration.

We've rounded up the best ELT and ETL tools for Snowflake and outlined each data integration tool's key features, pricing models, and pros and cons.

Why Use ETL With Snowflake?

ETL processes are needed to prepare data for loading into Snowflake. While Snowflake offers functionality for data transformation, it's not always as customizable as an ETL process, especially for managing complex schema data.
ETL helps automate the process of moving Snowflake data. This is particularly beneficial for big data use cases (e.g., data lakes) where data volume is high and manual ETL processes aren't feasible.
Real-time data processing in Snowflake might be preferred. ETL tools can support this data ingestion stage. While Snowflake offers real-time data ingestion, it may not be as robust or customizable.
Data security and privacy are critical to responsible data management. While Snowflake provides excellent data security, data pipelines offer additional compliance measures, such as schema validation, to mitigate potential data quality issues.

What to Look for in a Snowflake ETL Tool

With the Snowflake data cloud at the center of your modern data stack, the next step is choosing an ETL tool.

Here are the most important factors to consider in a Snowflake ETL tool: We recommend scoring potential ETL vendors with the following criteria:

Data sources

The best ETL tool has numerous data integrations for every API data source you need. More isn't always better; look for a data ingestion tool that supports your mission-critical apps and database formats.

Extensibility

As your needs grow, you'll probably expand your data sources. How easy will it be to get connectors for these sources? Can you count on the platform to build them for you, or will you need to develop them yourself? Open-source ETL tools are infinitely customizable, but it's all up to you to do it.

Optimization

Make sure you choose a tool that's been optimized specifically for Snowflake. Tools built with Snowflake in mind have better performance, work faster, and can result in lower pricing. Look for an ETL tool that follows best practices for Snowflake to lower costs and latency for everyday data warehousing tasks.

Data Warehouse Connectivity

For data engineering teams, connecting big data sets to a single data warehouse helps everyone work toward a common goal. Setting up disparate data lakes works against your orchestration objectives. Verify that your desired ELT tools connect to your cloud data warehouse provider, like Google BigQuery, Amazon Redshift, or Microsoft Azure.

Usability

Most tools have a tradeoff between power and ease of use. Do you have a technically proficient data engineering team that is happy to code each data transformation in Python manually? Or do you prefer a no-code tool with a drag-and-drop interface that works from day one, albeit for less complex workflows? The best Snowflake ETL tools are intuitive without an arduous onboarding period.

Support

How much help will you get with your extract, transform, and load pipelines? If something breaks, how quickly will the team respond (if at all)? Remember that some SaaS tools charge extra for premium support, which you should calculate into your budget.

Pricing

You'll need a tool that fits your budget now and encourages growth. Many data transformation tools adopt one of two pricing models: consumption-based or per-data flow. Consumption-based pricing can be hard to predict month-to-month and gets more expensive with more complex data.

On the other hand, pricing per data flow is predictable. Most companies have a finite number of long-tail data sources.

Top 5 Snowflake ETL tools

Portable
Apache Airflow
Stitch
Matillion
RudderStack

1. Portable

Portable is the best tool for performing ETL with Snowflake. It connects more than 500 SaaS apps, which you won't find with competing ETL tools.

Portable also develops custom data warehousing connectors at no extra cost and has lightning-fast turnaround times. Request a custom ETL connector, and you'll get it in a few days.

Ongoing maintenance is included, so your API integration works even if the underlying API changes.

Plus, Portable handles all monitoring, alerting, and automation so you can focus on the more impactful data analytics opportunities.

Pricing

Free: Portable's free plan allows manual data workflows without caps on volume, connectors, or data warehouses.
Paid: Automate your data flows for $200 monthly with unlimited data volume.
For enterprise requirements and SLAs, contact sales.

Key features

A vast catalog of 500+ no-code data connectors is ready to use today.
Custom data source connectors are built upon request at no extra cost.
Examples of popular Snowflake ETL connectors include Asana, Salesforce, and HubSpot.
Personalized support is available 24/7.

Disadvantages

Portable doesn't have connectors for some enterprise data sources you'll find with every other tool, like Oracle and Microsoft Azure.
At this time, data lakes are not supported.
Only available to customers based in the U.S.

Who is portable best suited for?

Portable is best for data teams with long-tail sources who want to spend time gathering data analytics insights, not scripting ETL pipelines.

2. Apache Airflow

Apache Airflow is an open-source ETL tool for Snowflake and other cloud data warehouses. It's a comprehensive tool that gives you complete control over your data with Python.

Pricing

Free: You'll manage Airflow's open-source platform on your own.

Key features

150+ data source connectors
Robust Python integrations that can create even highly complex workflows.
Highly extensible for users with technical knowledge.
Monitoring, scheduling, and management are all built into a single platform.

Disadvantages

Very technical, with extensive user setup required.
Knowledge of Python is mandatory for creating data pipelines in Airflow.

Who is Apache Airflow best suited for?

Teams with a high level of technical expertise that want an advanced open-source platform can host and manage themselves.

3.Stitch

Stitch is a Snowflake ETL tool that's part of the Talend ecosystem.

It handles data extraction and the fundamental transformations required to import it to your destination.

You can set up integrations with Python, Java, SQL, or the built-in GUI.

Pricing

Standard: Starts at $100/month for up to 5 million active rows per month, one destination, and 10 sources (limited to "Standard" sources).
Advanced: Starts at $1,250/month for up to 100 million rows and three destinations.
Premium: $2,500/month for up to 1 billion rows and five destinations
A 14-day free trial is available.

Key features

130+ data sources are supported, including Tableau and Power BI tools.
Easy-to-use platform with a graphical user interface.
Automation tools include monitoring and alerts.
Integration with the Talend suite of business intelligence tools

Disadvantages

Limited data transformation options.
On-premises SQL Server support requires a Singer tap.
Limited destinations with a maximum of one, three, or five, depending on the tiers.

Who is Stitch best suited for?

Stitch is best for teams that use the most common data sources and are looking for a no-code tool for data ingestion.

4. Matillion

Matillion is one of the most popular Snowflake data warehouse tools. It consists of two tools: Matillion Data Loader and Matillion ETL. (Despite the name, Matillion ETL uses an ELT pipeline.)

Pricing

Credit-based pricing that varies from month-to-month
Free: Up to one million rows per month
Basic: $2.00/credit
Advanced: $2.50/credit
Enterprise: $2.70/credit

Key features

110+ connections to popular SaaS apps
On-premises and cloud-based solutions are available.
Robust built-in tools for data transformation and orchestration.
More accessible data governance since the Data Loader and ELT tools are part of the same platform.

Disadvantages

There are no options for users to connect new data sources or adjust Matillion's built-in connectors.
Limited support for CSV output
It's harder to integrate third-party tools into your data stack.

Who is Matillion best suited for?

Matillion is best for organizations looking for an all-in-one data tool, especially one that offers on-premise deployments.

5. RudderStack

RudderStack is a customer data management service that recently acquired Blendo. It's designed to make data integration as easy as possible and has a fast setup process and automation scripts to eliminate manual work. RudderStack tends to skew more in favor of developers than less-technical users.

Pricing

Free: Up to three SDK sources and up to 1 million events
Starter: $500 monthly (billed annually: $6,000) for up to 3M events
Growth & Enterprise: Custom pricing for ETL pipeline functionality with dedicated support

Key features

180+ cloud-based data connectors
Real-time streaming customer data management
Python and JavaScript transformations with 10 data warehouse destinations

Disadvantages

Fewer data connectors compared to other Snowflake ETL tools
limited capabilities for data transformations.
Users can't create new data sources on their own.

Who is RudderStack best suited for?

RudderStack is best for organizations with extensive customer event data. Specifically, it is best for tech-savvy data analytics teams with few sources, and no transformation needs looking for an easy-to-use platform.

Runner-up Snowflake ETL tools

There are dozens of ETL tools compatible with the Snowflake data warehouse. Here are a few additional tools we didn't feature but might fit your needs well.

6. Fivetran

Fivetran is one of the industry leaders and supports more than 160 data sources. It's best for enterprise-level teams that need a robust, best-in-class platform and have a budget to match.

If you need a Fivetran custom connector that isn't available, Portable will build one for you.

7. Hevo

Hevo Data is a no-code platform that supports ETL, ELT, and reverse ETL workflows. It has 150+ data connectors and offers real-time data migration, replication, and built-in transformations.

8. Airbyte

Airbyte is an open-source data platform with more than 170 data connectors. It supports change data capture and warehouse-native transformations and lets users develop custom connections with its CDK.

9. Integrate.io

Integrate is a no-code platform that comes with templates for faster integrations. It supports 200+ data sources, especially those in e-commerce.

Snowflake ETL Overview

Snowflake is a SaaS data warehouse tool, not an ETL tool.
You can store and manage data within Snowflake, but you'll need a separate tool for the ETL (extract, transform, and load) process.
ETL is the modern replacement for traditional ELT (extract, load, transform) workflows.
Cloud data warehouses like Snowflake provide data engineering teams with near-infinite capacity.
Instead of transforming data with on-premises systems, today it's easier and cheaper to load data with Snowflake SQL and then transform it using cloud-based tools.

Why Snowflake is a Smart Choice for ETL

Snowflake is cloud-based storage for big data and supports ETL and ELT data pipelines. But it has plenty of competitors, including Google BigQuery, Microsoft Azure, Amazon Redshift, and other AWS platforms, to name a few.

Pricing

Snowflake's pricing follows a consumption model where you only pay for what you use.
Its pricing depends on data warehouse regions, job types, and your public cloud costs.
Four pricing tiers are available: standard, enterprise, business-critical, and virtual private Snowflake.

Key features

Snowflake separates its architecture into three parts: data storage, computing, and cloud services. These components are separated, letting you run several processes simultaneously on the same data set without waiting times.
Machine learning optimizations to find more efficient ways to handle ETL processes, meaning datasets load faster and more efficiently at a lower cost.
Unique SQL commands like UNDROP (to restore tables quickly) and CLONE (to duplicate without the cost or delay of copying)

Snowflake Pros & Cons

Snowflake Advantages	Snowflake Disadvantages
Cloud-agnostic warehouse supports Amazon Web Services, Google Cloud Platform, and Microsoft Azure.	Snowflake's daily loading limits can slow down bulk data migration.
Large data marketplace with hundreds of third-party apps.	The billing structure allows for unlimited use of services, which can result in unexpected charges at the end of the month.
Support for JSON functions within SQL.	Despite being cloud-agnostic, competitive data warehousing options exist in each cloud hosting provider.
Support for several types of encryption, including end-to-end and client-side.	Smaller developer community than open-source data integration tools, but it's growing rapidly.
Detailed and thorough documentation for data replication and analysis.

Who is Snowflake best suited for?

Snowflake is best for teams that want flexibility between cloud services and don't want to be tied down to a single ecosystem like AWS, Google Cloud, or Microsoft Azure. It's also a good fit for engineering-first teams with its unique SQL commands and complete documentation.

The Best ETL Tool for Snowflake: Portable

Snowflake is one of the top-rated data warehouses, making it a must-have technology for modern data analytics teams. It makes managing big data accessible for thousands of organizations worldwide.

These Snowflake data integrations have strengths, weaknesses, and novel approaches to simplifying data integration.

The easiest way to move your business data around is to use Portable. Without writing code, it lets you extract, transform, and load data from over 500 unique providers. Try it for free with unlimited data volumes and destinations. And when you're ready, automate data flow for just $200 per month.

Get your team focused on scaling your business intelligence objectives rather than dealing with the menial tasks of moving data from one place to another.