Introduction to Fivetran and Matillion
Modern companies rely on automated data pipelines to keep analytics and operations running smoothly. Two leading data integration tools—Fivetran and Matillion—take distinct approaches to moving, transforming, and managing data at scale. Fivetran is known for its fully managed ELT automation, requiring minimal configuration and oversight, while Matillion offers a platform with more hands-on visual workflows for ETL and ELT tasks. Both aim to simplify integrating data from diverse sources into cloud data warehouses, but the best fit depends on how much you want to customize, transform, or automate your pipelines.
Key Takeaways
- Fivetran emphasizes managed, no-code ELT with automated schema migration and minimal setup.
- Matillion provides visual ETL/ELT workflows with deeper in-platform transformation and orchestration options.
- Fivetran pricing is usage-based and scales with processed data, while Matillion uses a tiered subscription model.
- Both platforms support strong security and compliance standards, including SOC 2 Type II and GDPR.
| Feature | How Fivetran handles it | How Matillion handles it | Best for |
|---|---|---|---|
| Data Integration Approach | Fully managed, no-code ELT with automated schema migrations | Visual ETL/ELT workflows and deeper in-tool transformation | Fivetran: Fast automation; Matillion: Transformation control |
| Connectors | Varied selection; sync restrictions on some; exact numbers not publicly specified | Wide source/target options; exact numbers not publicly specified | Depends on connector needs (see pros/cons) |
| Limits & Scalability | Limited by monthly active rows and some connector sync frequencies | Limited by subscription tier (jobs, users); not row-capped | Fivetran: Simplicity; Matillion: Flexible scaling |
| Pricing Model | Usage-based; cost scales with data processed | Tiered subscriptions; pay by users/environments | Fivetran: Variable needs; Matillion: Predictable budgets |
| Security & Compliance | SOC 2 Type II, GDPR compliance | SOC 2 Type II, GDPR, role-based access controls | Both: Regulated industries |
| Cloud Platform Support | Not publicly specified | Not publicly specified | Depends on cloud choice |
Data Integration and Transformation Approaches
Fivetran focuses on delivering a fully managed, no-code ELT experience designed to minimize hands-on maintenance. Its service automatically handles schema migrations and infrastructure, allowing users to integrate data sources with little configuration or ongoing administration. This approach is ideal for organizations looking for rapid deployment, hands-free management, and reliable schema drift handling, but may not address complex transformation requirements within the pipeline itself.
Matillion offers a more customizable platform for both ETL and ELT. Users can build visually-driven workflows and control the order and type of transformations directly within the tool. This depth benefits organizations with complex data modeling needs or those who require extensive in-pipeline business logic before loading to a data warehouse. Matillion’s approach empowers data teams to orchestrate, customize, and document their data flows visually.
Connectors and Integration Coverage
Both Fivetran and Matillion support a wide array of connectors for cloud applications, databases, and file sources. While neither vendor publicly specifies the exact count or breakdown of available connectors, both platforms market extensive support for major data sources and targets. Fivetran does note, however, that some of its connectors are subject to synchronization frequency restrictions, which can impact how often data is refreshed for particular sources. Matillion’s connector landscape is flexible but details on possible limitations are not publicly specified. Both tools address the need for broad ecosystem support but should be evaluated case-by-case against your organization’s specific source and destination requirements.
Limits and Scalability
Fivetran sets its main usage limit through monthly active (processed) rows, with some connectors also imposing specific data sync frequency restrictions. This model ties your costs and throughput to the amount of data you regularly move, which can be a benefit for smaller data flows but may create cost unpredictability at higher data volumes or in fast-growing use cases.
Matillion uses subscription tiers to determine limits. These typically include caps on the number of active orchestration jobs, users, or deployed environments, but do not restrict based on the number of rows processed. As a result, Matillion can offer more predictable resource allocation for larger organizations updating many large datasets, provided those needs fit within subscription tier boundaries. Scaling decisions with Matillion are often focused on user and workflow growth instead of raw data volume.
Pricing Models and Budgeting Impact
Fivetran charges on a usage-based model, where costs scale directly with the volume of data rows processed and synced monthly. There is no flat monthly fee model, so organizations with variable or growing data needs may see costs fluctuate. This structure can allow you to start small, but should be carefully modeled if significant data growth is expected.
Matillion provides clear, tiered subscription pricing. Customers pay based on the number of users, environments, or the tier’s feature set—often making upfront budgeting more straightforward. This can be advantageous for IT finance teams that need predictable annual or monthly billing, especially in organizations with steady-state or gradually scaling data integration requirements.
Security Standards and Compliance
Both Fivetran and Matillion place a high emphasis on meeting industry security and privacy standards:
- Fivetran: Adheres to SOC 2 Type II and GDPR compliance, with strict focus on data movement security during extraction, transfer, and loading phases. Security features are embedded throughout the pipeline for regulated industries.
- Matillion: Aligns with SOC 2 Type II, GDPR, and provides robust role-based access controls for platform users. Security is integrated into both orchestration and transformation phases, supporting enterprise and compliance-driven customers.
Cloud Platform Support
Specific supported cloud platforms for either Fivetran or Matillion are not publicly specified, but both are widely presented as being compatible with major public cloud vendors and typical enterprise data environments. For companies adopting a multi-cloud or hybrid-cloud strategy, it’s advisable to confirm platform compatibility for your specific sources, targets, and hosting preferences before making a final selection. Each vendor typically lists up-to-date platform support on their official product documentation.
Setup, Hosting, and Administration
Fivetran excels in rapid setup and low ongoing administration thanks to its managed, automated approach. It abstracts away infrastructure management and focuses on hands-off operation. Matillion, in contrast, requires more initial configuration and ongoing adjustment due to its visual and customizable workflow engine, but delivers greater flexibility and in-pipeline control. Administration experience depends on your desired balance between simplicity and customization.
When to Choose Fivetran vs Matillion
Choose Fivetran if you need quick, reliable data movement from many sources with minimal configuration and don’t require complex in-pipeline transformation logic. It’s suitable for teams with limited data engineering resources, or those prioritizing ELT and hands-free operation.
Opt for Matillion if your organization values detailed control over workflow orchestration, in-tool data transformation, or needs to build sophisticated business logic into your integration pipelines. Matillion is also appealing for teams with predictable pipeline needs or requiring collaborative, multi-user workflow design.
For both tools, confirm connector support and compliance alignment with your data stack and regulatory requirements before deciding.
Conclusion
Fivetran and Matillion are both reputable options for building modern data integration stacks. Fivetran’s strength lies in fully managed ELT with minimal setup and effortless scaling tied directly to usage. Matillion stands out for its deeper transformation capabilities and transparent, tiered pricing. Your choice comes down to whether you need simplicity and automation above all, or are looking for transformation and orchestration breadth within your data integration platform.
FAQs
Which is better for ETL: Fivetran or Matillion?
Matillion is better suited for traditional ETL because it enables extensive in-tool data transformations and workflow control. Fivetran, while offering some transformation, is designed for ELT with automation and minimal manual setup.
How do the pricing models of Fivetran and Matillion compare?
Fivetran uses a usage-based model based on data processed, resulting in variable costs. Matillion employs a tiered subscription structure, making budgeting more predictable.
What compliance standards do Fivetran and Matillion meet?
Both platforms are SOC 2 Type II and GDPR compliant. Matillion also emphasizes robust role-based access controls.
Which tool has broader connector support: Fivetran or Matillion?
Exact connector counts are not publicly specified, but both support a wide range of sources and targets. Fivetran notes sync frequency restrictions for some connectors.
Can both Fivetran and Matillion be integrated with major cloud platforms?
Specific platforms are not listed, but both are designed for widespread cloud compatibility. Check with vendors for up-to-date support details.
How do Fivetran and Matillion differ in terms of data transformation features?
Fivetran focuses on automated ELT with limited in-tool transformation. Matillion offers robust, visual transformation and orchestration within the platform.
What are the pros and cons of using Fivetran vs Matillion?
Fivetran: Pro—fully managed, minimal setup. Con—less transformation depth. Matillion: Pro—visual workflows, deep transformation. Con—more administration and higher setup effort.