Top Snowflake Etl Tools: Benefits, Features, Pricing

ETL tools streamline the process of moving data into Snowflake — they make it cleaner, more organized, and ready for insights at a faster pace. But how to choose the right tool between Snowflake ETL tools? This guide will help you make a decision.

Why use ETL with Snowflake?

Using ETL in Snowflake makes a huge difference in how you manage and make sense of your data:

• ETL tools automate the process of extracting data from various sources, transform it into a usable format, and load it into Snowflake.
• They help cleanse and standardize data before it reaches Snowflake.
• ETL tools preprocess the data to reduce the load on Snowflake and lead to faster query performance and quicker insights.
• ETL for Snowflake tools scales with your business as they easily handle increasing volumes of data.
• ETL tools ensure that your data is in the right shape and place for complex analyses.
• Efficient data processing means less storage and computing power needed, leading to lower costs.
• The best ETL tools offer extensive integration options, so you can connect Snowflake with different data sources and business applications.

Integrating ETL tools with Snowflake can boost the overall value you get from your data — it improves data quality, enhances performance, and drives insights at scale.

List of the Best Snowflake ETL Tools

The best ETL tools for Snowflake include:

• Shipyard;
• Matillion;
• Fivetran;
• Apache Airflow;
• StreamSets.

Let’s explore these ETL tools for Snowflake one by one.

TOP 1. Shipyard

Shipyard is a cloud-based platform that automates and streamlines data workflows with minimal coding required. It enables businesses to rapidly build and deploy custom data pipelines, integrating with Snowflake and other data sources.

Key Features

• Shipyard’s library includes over 100 pre-built templates.
• The platform’s drag-and-drop interface and simple scripting reduce the need for in-depth coding knowledge.
• Supports both event-driven and scheduled workflows, with fine-tuned scheduling options down to the minute.
• Offers detailed logs and the option to set up custom alerts via email or webhook.

Pros and Cons

Pros:

• Speed of deployment — minutes after signing up for basic projects, and a few days for more complex pipelines.
• Shipyard’s platform allows for the creation of highly customized workflows that adapt to almost any use case.
• The platform enables even those with minimal technical expertise to build and manage data pipelines.

Cons:

• The platform is relatively new so there may be occasional growing pains.
• The platform’s simplicity is limited for certain complex data engineering tasks.

Pricing and Integration

Plans start at $500 per month for startups. More comprehensive plans (including unlimited tasks and executions, and premium support) scale up to $2,000 per month or more. Large enterprises or users with very high volumes of data may require custom quotes.

Direct integration with Snowflake is done by native connectors. Shipyard also supports integration with other popular data sources like Salesforce, Google Analytics, and AWS S3, as well as databases like PostgreSQL and MySQL.

Use Cases

Marketing teams can automate weekly data collection from various sources, including social media and sales platforms, transforming this data with Shipyard before loading it into Snowflake for analysis.

Financial services firms can implement a daily Shipyard workflow to aggregate transaction data from multiple systems, apply fraud detection algorithms, and update Snowflake datasets used by their analytics team for real-time monitoring.

TOP 2. Matillion

Matillion is a powerful, cloud-native ETL solution that transforms raw data into analytics-ready formats.

Key Features

• The graphical user interface simplifies the design and execution of ETL jobs with over 120 pre-built components for data transformation and loading, promoting a code-free or low-code experience.
• The platform uses cloud data warehouses’ scalable compute resources, capable of processing billions of rows of data.
• Offers advanced job scheduling and orchestration features, so that users can automate ETL workflows across different environments.

Pros and Cons

Pros:

• High performance with some noting reductions in data transformation times from hours to minutes.
• Due to connectors to over 100 different data sources and platforms, the service facilitates the ingestion of various data types directly into Snowflake.
• Regular updates and new feature releases.

Cons:

• The consumption-based pricing model may be challenging to predict and manage.
• There’s still a learning curve for users aiming to use the platform’s more advanced capabilities.

Pricing and Integration

Base monthly fees start from around $1,000, while Advanced and Enterprise plans cost about $2,500 and $2,700 consequently.

Matillion’s native integration with Snowflake ensures optimized data transfer and transformation, including specialized features like Snowflake’s bulk loading and external stage support.

Use Cases

In digital marketing, the platform automates the daily ingestion and transformation of gigabytes of ad performance data across multiple clients into Snowflake. This process cuts down their reporting turnaround time from days to mere hours.

Healthcare analytics companies can use Matillion to process and anonymize patient data from various sources before loading it into Snowflake, complying with HIPAA regulations while enabling advanced research analytics.

TOP 3. Fivetran

Fivetran is a data integration service designed to automate the extraction, loading, and transformation of data into a data warehouse or database for analysis.

Key Features

• Fivetran’s zero-maintenance pipelines automatically adapt to schema changes.
• Offers 150+ pre-built connectors for databases, applications, and events.
• Integrates with dbt (data build tool) to offer transformation capabilities within a data warehouse, applying business logic to raw data.

Pros and Cons

Pros:

• Handles millions to billions of rows of data daily.
• Implements strong security measures, including end-to-end encryption and compliance with data protection regulations.
• Customers report up to a 90% reduction in time spent managing data pipelines.

Cons:

• The consumption-based model may lead to unpredictability in monthly billing.
• The trade-off for ease of use and automated maintenance is reduced flexibility in customizing integration logic compared to hand-coded pipelines.

Pricing and Integration

Fivetran’s pricing starts at a base rate for access to the platform, plus additional fees based on the volume of data synced. For example, a small to medium-sized business may spend around $1,000 to $2,000 per month depending on their data usage and selected connectors. Larger enterprises with extensive data requirements may invest tens of thousands monthly to support their operations.

Direct “Plug & Play” integration with Snowflake allows for setup times as short as five minutes for some connectors. This quick integration process relies on using existing API keys or authentication methods, so the data syncing begins almost immediately.

Use Cases

In healthcare analytics, Fivetran can be used to consolidate patient records, treatment outcomes, and operational data from multiple sources into Snowflake. Analyzing data across these vectors helps identify trends in patient care and operational efficiency.

Digital media companies can integrate clickstream data, social media interactions, and subscription information using Fivetran, and then centralize this data in Snowflake to analyze content performance and audience engagement in real-time.

TOP 4. Apache Airflow

Apache Airflow is an open-source platform used for orchestrating complex computational workflows and data processing pipelines. It programmatically authors, schedules, and monitors workflows as Directed Acyclic Graphs (DAGs), where each node in the graph represents a task.

Key Features

• Programmatic workflow management.
• Dynamic task generation based on parameters and conditions.
• A rich user interface for monitoring and managing workflows, including task progress and scheduling.
• Extensible architecture that supports custom operators, executors, and hooks.
• Scalability and handling a large number of tasks and complex workflows using executor models like Celery, Kubernetes, and others.

Pros and Cons

Pros:

• Flexibility and customization.
• Strong community support.
• Comprehensive monitoring and logging.

Cons:

• A steep learning curve (requires the knowledge of Python).
• Resource intensive, especially for larger workflows.
• Complexity in dynamic DAGs.

Pricing and Integration

Apache Airflow is an open-source tool, so it’s free to use. However, deploying and maintaining Airflow on cloud platforms or in an enterprise environment sometimes incurs costs related to computing resources, development, and operations.

Apache Airflow successfully integrates with Amazon AWS, Google Cloud Platform, Microsoft Azure, databases, message queues, and more.

Use Cases

In data engineering, Apache Airflow can be used to automate daily ETL tasks, aggregating data from various sources, transforming it, and loading it into a data warehouse for analysis.

It can also automate the entire lifecycle of machine learning models, from data preprocessing and training to prediction and serving.

TOP 5. Streamsets

StreamSets builds, runs, monitors, and manages data flows across various architectures, from traditional on-premises data systems to complex cloud-based or hybrid environments.

Key Features

• Processes millions of data points per second and adjusts in real-time to changes in a data structure or schema, known as ‘data drift’.
• Over 100 pre-built connectors and operators for integration with databases (SQL, NoSQL), cloud storage (AWS S3, Azure Blob Storage), streaming services (Apache Kafka, Google Pub/Sub), and more.
• Means low-latency data processing and is suitable for applications requiring real-time analytics.

Pros and cons

Pros:

• Successfully deployed in environments processing over a billion transactions daily.
• Offers detailed operational insights through dashboards that track metrics like throughput, latency, and error rates.

Cons:

• Large-scale deployments require significant computing and memory resources.

Pricing and integration

Pricing plans are customized, but businesses can start with a free version for basic data movement tasks.

StreamSets successfully integrates with platforms such as AWS, Google Cloud Platform, and Azure, along with support for Kafka, Hadoop, NoSQL databases, and more

Use Cases

Retail companies can use StreamSets to ingest customer behavior data from their online platforms in real time for personalized marketing actions.

It can also be used in the financial sector to manage sensitive financial data across on-premises databases and cloud analytics platforms.

Let’s Choose the Right Snowflake ETL Tool with Visual Flow

Visual Flow will help you find the right ETL tool for Snowflake. We specialize in services such as ETL migration consulting — and that’s why it’s best to contact us to choose a Snowflake-compatible tool that’s both cost-effective and scalable. You can also explore our best practices for ETL data modeling to understand that we are all in on making your data work smarter for you.
However, there are some general tips for choosing the right Snowflake ETL tool:

• Understand the types of data you’re working with and where it’s coming from.
• Get a clear picture of how you want your data to flow from source to Snowflake.
• Think about how much data you’re dealing with and how fast it’s growing.
• Choose a tool that feels intuitive and doesn’t require you to be a coding pro to use it.
• Make sure the tool integrates with Snowflake and any other platforms you use.
• Choose a tool that can grow with you.
• If possible, test out the tool or see it in action.

And don’t hesitate to ask Visual Flow’s help if you have any queries or concerns.

Final Thoughts

To choose the best ETL tool for Snowflake, consider how the tool fits into your team’s daily life, simplifies your workflows, and helps you make sense of your data in new and exciting ways. Remember that Visual Flow is by your side in solving this task.

TOP 3. Fivetran

Fivetran is a data integration service designed to automate the extraction, loading, and transformation of data into a data warehouse or database for analysis.

Key Features

• Fivetran’s zero-maintenance pipelines automatically adapt to schema changes.
• Offers 150+ pre-built connectors for databases, applications, and events.
• Integrates with dbt (data build tool) to offer transformation capabilities within a data warehouse, applying business logic to raw data.

Pros and Cons

Pros:

• Handles millions to billions of rows of data daily.
• Implements strong security measures, including end-to-end encryption and compliance with data protection regulations.
• Customers report up to a 90% reduction in time spent managing data pipelines.

Cons:

• The consumption-based model may lead to unpredictability in monthly billing.
• The trade-off for ease of use and automated maintenance is reduced flexibility in customizing integration logic compared to hand-coded pipelines.

Pricing and Integration

Fivetran’s pricing starts at a base rate for access to the platform, plus additional fees based on the volume of data synced. For example, a small to medium-sized business may spend around $1,000 to $2,000 per month depending on their data usage and selected connectors. Larger enterprises with extensive data requirements may invest tens of thousands monthly to support their operations.

Direct “Plug & Play” integration with Snowflake allows for setup times as short as five minutes for some connectors. This quick integration process relies on using existing API keys or authentication methods, so the data syncing begins almost immediately.

Use Cases

In healthcare analytics, Fivetran can be used to consolidate patient records, treatment outcomes, and operational data from multiple sources into Snowflake. Analyzing data across these vectors helps identify trends in patient care and operational efficiency.

Digital media companies can integrate clickstream data, social media interactions, and subscription information using Fivetran, and then centralize this data in Snowflake to analyze content performance and audience engagement in real-time.

TOP 4. Apache Airflow

Apache Airflow is an open-source platform used for orchestrating complex computational workflows and data processing pipelines. It programmatically authors, schedules, and monitors workflows as Directed Acyclic Graphs (DAGs), where each node in the graph represents a task.

Key Features

• Programmatic workflow management.
• Dynamic task generation based on parameters and conditions.
• A rich user interface for monitoring and managing workflows, including task progress and scheduling.
• Extensible architecture that supports custom operators, executors, and hooks.
• Scalability and handling a large number of tasks and complex workflows using executor models like Celery, Kubernetes, and others.

Pros and Cons

Pros:

• Flexibility and customization.
• Strong community support.
• Comprehensive monitoring and logging.

Cons:

• A steep learning curve (requires the knowledge of Python).
• Resource intensive, especially for larger workflows.
• Complexity in dynamic DAGs.

Pricing and Integration

Apache Airflow is an open-source tool, so it’s free to use. However, deploying and maintaining Airflow on cloud platforms or in an enterprise environment sometimes incurs costs related to computing resources, development, and operations.

Apache Airflow successfully integrates with Amazon AWS, Google Cloud Platform, Microsoft Azure, databases, message queues, and more.

Use Cases

In data engineering, Apache Airflow can be used to automate daily ETL tasks, aggregating data from various sources, transforming it, and loading it into a data warehouse for analysis.

It can also automate the entire lifecycle of machine learning models, from data preprocessing and training to prediction and serving.

TOP 5. Streamsets

StreamSets builds, runs, monitors, and manages data flows across various architectures, from traditional on-premises data systems to complex cloud-based or hybrid environments.

Key Features

• Processes millions of data points per second and adjusts in real-time to changes in a data structure or schema, known as ‘data drift’.
• Over 100 pre-built connectors and operators for integration with databases (SQL, NoSQL), cloud storage (AWS S3, Azure Blob Storage), streaming services (Apache Kafka, Google Pub/Sub), and more.
• Means low-latency data processing and is suitable for applications requiring real-time analytics.

Pros and cons

Pros:

• Successfully deployed in environments processing over a billion transactions daily.
• Offers detailed operational insights through dashboards that track metrics like throughput, latency, and error rates.

Cons:

• Large-scale deployments require significant computing and memory resources.

Pricing and integration

Pricing plans are customized, but businesses can start with a free version for basic data movement tasks.

StreamSets successfully integrates with platforms such as AWS, Google Cloud Platform, and Azure, along with support for Kafka, Hadoop, NoSQL databases, and more

Use Cases

Retail companies can use StreamSets to ingest customer behavior data from their online platforms in real time for personalized marketing actions.

It can also be used in the financial sector to manage sensitive financial data across on-premises databases and cloud analytics platforms.

Let’s Choose the Right Snowflake ETL Tool with Visual Flow

Visual Flow will help you find the right ETL tool for Snowflake. We specialize in services such as ETL migration consulting — and that’s why it’s best to contact us to choose a Snowflake-compatible tool that’s both cost-effective and scalable. You can also explore our best practices for ETL data modeling to understand that we are all in on making your data work smarter for you.
However, there are some general tips for choosing the right Snowflake ETL tool:

• Understand the types of data you’re working with and where it’s coming from.
• Get a clear picture of how you want your data to flow from source to Snowflake.
• Think about how much data you’re dealing with and how fast it’s growing.
• Choose a tool that feels intuitive and doesn’t require you to be a coding pro to use it.
• Make sure the tool integrates with Snowflake and any other platforms you use.
• Choose a tool that can grow with you.
• If possible, test out the tool or see it in action.

And don’t hesitate to ask Visual Flow’s help if you have any queries or concerns.

Final Thoughts

To choose the best ETL tool for Snowflake, consider how the tool fits into your team’s daily life, simplifies your workflows, and helps you make sense of your data in new and exciting ways. Remember that Visual Flow is by your side in solving this task.