7 Top Snowflake ETL Tools For 2025 (G2 Ratings)

Are you trying to understand better the plethora of Snowflake ETL tools available in the market to see if they fit your bill? Are you a Snowflake customer (or planning on becoming one) looking to extract and load data from various sources? If any of the above questions apply, you have stumbled upon the right article.

This article specifically compares and answers the best ETL tools for Snowflake that can move data into the Snowflake data warehouse. It’ll also go over some factors you should consider when looking for Snowflake tools.

What is Snowflake?

Snowflake is a fully managed SaaS that provides one platform for data warehousing, data lakes, data engineering, data science, and data application development while ensuring the secure sharing and consumption of real-time/shared data. It offers a cloud-based data storage and analytics service called data warehouse-as-a-service. Organizations can use it to store and analyze data using cloud-based hardware and software.

Key Features of Snowflake

• Data Governance and Compliance: Advanced security features incorporate end-to-end encryption, complying with regulations.
• Multi-cluster shared data architecture: It allows point-to-point scaling of computing resources independent of storage.
• Separate Storage and Compute: Optimizes cost performance with the ability to scale storage and compute independently.
• Data Security with Sharing: Enables data sharing in real-time without losing privacy and security.

What is Snowflake ETL?

ETL stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and loaded into a target database or data warehouse. The sources may include flat files, third-party applications, databases, etc.

Snowflake ETL means applying the ETL process to load data into the Snowflake data warehouse. This comprises extracting relevant data from data sources, making necessary transformations to make the data analysis-ready, and then loading it into Snowflake.

Snowflake ETL Highlights:

• Snowflake minimizes the need for lengthy, risky, and labor-intensive ETL processes by enabling secure data sharing and collaboration with internal and external partners.
• It supports both traditional ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) approaches, providing flexibility in data integration workflows.
• The Snowpark developer framework allows data engineers, scientists, and developers to execute data processing pipelines and feed ML models faster and more securely within the Snowflake platform, using languages like Python, Java, and Scala.
• Snowflake’s easy ETL/ELT options allow data engineers to focus on critical data strategy and pipeline optimization projects rather than manual coding and data cleaning tasks.
• By leveraging Snowflake as a data lake and data warehouse, the need for pre-transformations and pre-schemas is eliminated, effectively streamlining the ETL process.

Why Do You Need Snowflake ETL?

If you are pondering investing in a new data warehouse, Snowflake is a proven solution that comes with a lot of handy features. These would be enough reasons to start setting up ETL for Snowflake. Here are some of them:

• Decoupled Architecture: Snowflake architecture consists of three layers – storage, compute, and cloud services. Because they are decoupled, it allows for independent scaling up/down of these layers. As a result, it removes any requirement to pre-commit to a set of resources, as is the case with the traditional, unified architecture.
• JSON using SQL: The ability to work with JSON data is a lot like querying traditional structured data using a set of types and functions like variant, parse_json, etc.
• UNDROP and Fast Clone: Using the UNDROP SQL command, you can bring back a dropped table without waiting for it to be restored from a backup. Fast Clone is a feature that lets you clone a table or an entire database, typically in seconds, at no additional service cost.
• Encryption: Snowflake comes with many encryption mechanisms, such as end-to-end encryption, client-side encryption, etc., ensuring a high level of data security at no additional cost.
• Query Optimization: There are query optimization engines that run in the background to understand and automatically improve query performances. This lets the SQL scripters not worry about the optimization practices such as indexing, partitioning, etc. 

What to Look for in a Snowflake ETL Tool?

When selecting an ETL tool, prioritize these key factors:

1. Data Sources: Choose a tool with integrations for your essential apps and database formats.
2. Extensibility: Ensure the tool can quickly expand to new data sources as your needs grow.
3. Optimization: Opt for a tool optimized for Snowflake to enhance performance and reduce costs.
4. Data Warehouse Connectivity: Verify compatibility with your cloud data warehouse, like AWS Redshift or Google BigQuery.
5. Usability: Balance between ease of use and the power your team needs, whether it’s no-code or requires coding.
6. Support: Consider the level of support provided, especially for critical issues.
7. Pricing: Select a pricing model that fits your budget and scales with your needs.

7 Best Snowflake ETL Tools

Choosing the ideal ETL tool that perfectly meets your business requirements can be challenging, especially when a large variety of Snowflake ETL tools are available in the market. To simplify your search, here is a comprehensive list of the seven best tools for Snowflake ETL that you can choose from and start setting up ETL pipelines with ease:

1. Hevo Data

G2 Ratings: 4.4 out of 5 stars (260)

Hevo is one of the best Snowflake tools that allows you to replicate data in near real-time from 150+ sources to Snowflake without writing a single line of code. Finding patterns and opportunities is easier when you don’t worry about maintaining the pipelines. So, with Hevo as your data pipeline platform, maintenance is one less thing to worry about.

For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more.

Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.

– Juan Ramos, Analytics Engineer, Ebury

Check out how Hevo empowered Ebury to build reliable data products here.

Key Features

• Exceptional Security: A fault-tolerant architecture that ensures zero data loss.
• Built to Scale: Exceptional horizontal scalability with minimal latency for modern-data needs.
• Built-in Connectors: Support for 150+ data sources, including databases, SaaS platforms, files, and more. Native webhooks and a REST API connector are available for custom sources.
• Incremental Data Load: Hevo allows the transfer of modified data in real-time, ensuring efficient bandwidth utilization on both ends.
• Auto Schema Mapping: Hevo eliminates the tedious task of schema management. It automatically detects the format of incoming data and replicates it to the destination schema. You can also choose between full and incremental mappings to suit your data replication requirements.
• Blazing-fast Setup: Straightforward interface for new customers to work on, with minimal setup time.
• Live Support: The Hevo team is available round the clock to extend exceptional customer support through chat, email, and support calls.

Pricing

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has tier-based pricing plans starting with a free tier, where you can ingest upto 1 million records.

2. Stitch

G2 Ratings: 4.4 out of 5 stars (68)

Stitch is a user-friendly tool that simplifies data integration by extracting, transforming, and loading data into Snowflake and other data warehouses. It is known for its ease of use and extensive range of pre-built integrations.

Stitch integrates and syncs your data to Amazon Redshift, Google BigQuery, Microsoft SQL Server, Snowflake, and PostgreSQL.

Key Features

• Stitch connects to numerous data sources, including databases and SaaS applications.
• It transfers only new or updated data to optimize performance with incremental data loading.
• It is easy to configure without extensive technical skills.
• Stitch provides an easy-to-use dashboard for tracking ingested and synced data. 
• Product documentation is available as a knowledge base on the company website.

Limitations

• Stitch Data uses row-based pricing, which makes its pricing high when working with large amounts of data. 
• Their technical support is slow to respond, leading to delays in solving issues and data integration.

Pricing

• Stitch offers transparent and predictable pricing. It offers three pricing tiers to meet various requirements: Standard, Advanced, and premium.
• The pricing plan starts from 100$ for its standard version. More details on pricing are available here.

Best Suited Use Case

Stitch is ideal for small to medium-sized businesses needing a simple ETL solution with common integrations. It’s best for those who want quick setup and incremental data updates.

3. Matillion

G2 Rating: 4.4 out of 5 stars (77)

Matillion is another solution specifically built for cloud data warehouses is Matillion.
So, if you want to load data into Amazon Redshift, Google BigQuery, or Snowflake, it could be a good option for you.

Matillion ETL allows you to perform powerful transformations and combine transformations to solve complex business logic. You can use scheduling orchestration to run your jobs when resources are available.

Key Features

• It comes with two product offerings: Data Loader and Matillion ETL. Data loader is an easy-to-use, GUI-based cloud solution that loads data into data warehouses. Matillion ETL includes data transformation options for the source data before loading it into the data warehouse.
• The data transformations can be accomplished via custom SQL or by creating transformation components using the GUI.
• It supports over 70 data sources, including databases, CRM platforms, social networks, etc.
• Customer support is available through an online ticketing system and over the phone.
• Documentation is available as articles tailored towards specific data warehouses and for the Data Loader product.

Limitations

• Live chat support is not available.
• Users cannot independently add a new data source (or tweak an existing one).

Pricing

• Data Loader is free of charge, and Matillion ETL comes with a 14-day free trial.
• The basic plan for Matillion ETL is priced at an approximate annual cost of $12000.

Best Suited Use Case

Matillion offers the flexibility of two versions of its product, one free of cost. Matillion ETL is relatively expensive; however, it supports an extensive list of input sources covering all significant databases, popular social media platforms, and an array of SaaS products.

It can be one of the ideal choices for your Snowflake ETL tools if the features mentioned above meet your requirements.

4. StreamSets

G2 Rating: 4.0 out of 5 stars (99)

StreamSets Data Collector is open-source software that allows you to build enhanced data ingestion pipelines for Elasticsearch. These pipelines can adapt automatically to schema, infrastructure, and semantics changes.

Like Matillion, StreamSets is also one of the Snowflake ETL tools, and it has two versions: Data Collector (focused on moving data from source to destination) and Transformer (to perform comprehensive ETL, powered by Apache Spark clusters).

Key Features

• It provides a drag-and-drop GUI to perform transformations such as lookup, add, remove, typecast, etc., before loading data into the destination.
• It allows customers to add new data sources on their own. Custom data processors can be written in JavaScript, Groovy, Scala, etc.
• It supports over 50 data sources, including databases and streaming sources like Kafka and MapR.
• Customer support is available through an online ticketing system as well as over-call.
• Extensive product and operational documentation are available on the company website.

Limitations

• Live customer chat support is not available.
• It lacks extensive coverage of SaaS input sources.

Pricing

• It offers a 30-day free trial.
• Basic pricing options for this Snowflake ETL tool are not directly available on the company website. You can get in touch with their team to know more about pricing.

Best Suited Use Case

StreamSets is one such tool that is particularly well-suited for users with a lot of event and file streaming sources. It also provides options for users to make changes to the input sources, unlike other completely off-the-shelf products, so this aligns well with teams that can work to customize their ETL process technically.

5. Apache Airflow

G2 Rating: 4.3 out of 5 stars (87)

Apache Airflow is an open-source workflow orchestration tool used to programmatically author, schedule, and monitor complex data pipelines. It is widely used for automating ETL processes, managing data workflows, and integrating with various data sources and services.

Key Features

• Dynamic Workflows: Create workflows as Python code for flexibility.
• Scheduling & Monitoring: Built-in scheduler and web-based UI.
• Extensible Integrations: Supports plugins and external system connections.
• Task Management: Manages dependencies using Directed Acyclic Graphs (DAGs).
• Scalability: Supports distributed execution with Celery, Kubernetes, etc.

Limitations

• Requires advanced configuration and infrastructure management.
• Steep Learning Curve: Not ideal for non-technical users.
• Primarily an orchestration tool, not a full ETL solution.

Pricing

• Free since it is open-source tool.

Best Suited Use Case

Apache Airflow is ideal for orchestrating complex, batch-oriented data pipelines, scheduling ETL workflows, and managing data engineering and machine learning pipelines, especially in Python-centric environments.

6. Fivetran

G2 Rating: 4.2 out of 5 stars (409)

Fivetran is a fully managed data integration platform that automates ETL/ELT processes by seamlessly connecting 300+ data sources to cloud data warehouses like Snowflake, BigQuery, and Redshift. It offers real-time data syncing, schema mapping, and minimal maintenance, making it ideal for analytics and reporting.

Key Features

• Automated Data Integration: Pre-built connectors for 300+ data sources.
• Zero-Maintenance ETL: Fully managed pipelines with automated schema mapping and updates.
• Data Transformation: Supports SQL-based transformations with dbt (Data Build Tool) integration.
• Real-Time Data Sync: Near real-time data replication for accurate analytics.

Limitations

• Very expensive and opaque pricing model.
• Pre-built connectors may not suit niche or highly customized data sources.

Pricing

Fivetran offers a usage-based pricing model, primarily based on Monthly Active Rows (MAR). Pricing varies by data volume, connector type, and features used. A 14-day free trial is available, but detailed pricing requires direct inquiry.

Best Suited Use Case

Apache Airflow is a typical open-source ETL tool, the use of which involves complex coding for setup. For companies looking to develop and manage a custom Snowflake ETL tool in-house using a fairly mature open-source product, Airflow is worth checking out. 

7. Integrate.io

G2 Rating: 4.3 out of 5 (197)

Integrate.io is a specialized data warehouse platform built for e-commerce businesses. It is a ready-to-use native snowflake connector supporting over 200 data sources. Unlike other Snowflake ETL tools, it facilitates no-code solutions and empowers you to implement custom transformation jobs from diverse data sources.

Key Features

• You can schedule ETL jobs based on your terms, with the ability to run data processes as you like.
• Data transformations and data flows are made easy regardless of schema.
• Enhanced data security and compliance. 

Limitations

Debugging can be an overhead thus you need to go through the error log to identify the root issue.

Pricing

Charges are based on connector, not data volume, which could work out cheaper.

Best Suited Use Case

Integrate.io is the best choice for Snowflake ETL in case of an e-commerce enterprise with many incoming data sources, analytics, and heavy decision-making.

Factors to Consider while Evaluating Snowflake ETL Tools

There are several plug-and-play as well as heavily customizable Snowflake ETL tools to move data from a variety of Data Sources into Snowflake.

Every business needs to prioritize certain things over others in deciding to invest in the right ETL Snowflake product for its operations. Here are some factors that need to be considered for evaluating such products:

• Paid or Open-Source: Cost is always a concern – the choice here would be between in-house custom development or utilizing the expertise of a reputed ETL with a Snowflake service provider.
• Ease of Use: This can vary from simple drag-and-drop GUIs to writing SQL or Python scripts to enable complex transformations in the ETL process.
• Ability to move Data from a Wide Array of Data Sources: Ideally, you would want one service provider to service all your Data Engineering and ETL needs. Hence, in terms of the number of Data Sources, the more the merrier.
• Option for Adding/Modifying Data Sources: Most ETL service providers support a fixed set of Data Sources. In case you need to leave room for custom additions of new sources, you need to make sure that this is an option.
• Ability to Transform the Data: Some tools focus on extracting and loading data and may have zero to very few transformation options. Hence, it is important to understand the level of data transformation supported by the ETL product.
• Pricing: Price depends on a range of factors and use cases. It is important to clearly understand your ETL requirements while evaluating different service providers to maximize the bang for your buck.
• Product Documentation: Even when reliable customer support is available, it can be useful to have access to detailed documentation for in-house engineers to tweak or troubleshoot something quickly.
• Customer Support: Timely, efficient, and multi-channel customer support is quite important in this whole process.

You can also take a look at the best data extraction tools to help you decide the right tool that fits your needs.

Conclusion

This blog discussed the seven best ETL tools for your Snowflake data warehouse. Apart from the ones discussed above, there are even more tools available in the market. This is a clear indicator of a huge market for ETL and that many companies are comfortable outsourcing their ETL needs to these providers.

Companies want to invest more time and resources in running analytics and generating insights from their data and less in moving data from one place to another.

The process needs to be planned and executed, considering some essential points to complete it efficiently. You need to know the vital Snowflake ETL best practices while migrating data to the Snowflake Cloud Data Warehouse.

If you’re looking for an all-in-one ETL Tool, that will not only help you transfer data but also transform it into analysis-ready form, then Hevo Data is the right choice for you! Hevo will take care of all your ETL, data integration, analytics, and data ingestion needs in a completely automated manner, allowing you to focus on key business activities. Sign up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

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