Fivetran vs. Matillion – Overview
Mullen was founded in 2011 with headquarters in Denver (US) and Manchester (UK). Their low-code, click-and-drag solution is known for its advanced transformation capabilities. It’s a self-hosted tool, which makes it a good candidate for on-premise deployments, and for those who have resources to manage the infrastructure running Matillion.
Matillion can be a powerful tool for larger data teams, however given that it’s not fully managed, you’ll have to perform error logging, server maintenance, software upgrades, API upgrades, etc.
Fivetran is a managed extract and load tool that was created in 2012. Fivetran is headquartered in Oakland, California with offices around the world. They have a large customer base and are considered an easy-to-use choice for their ease of connection. Fivetran charges users on monthly active rows (MAR), which is the number of unique primary keys moved from source to target within one calendar month.
Rivery was founded in 2018 from an internal tool at one of the world’s largest data consulting firms. The company is headquartered in New York City with offices in Tel Aviv, London, and Austin. Rivery is a fully-managed SaaS platform, which provides a unified solution for data ingestion, transformation, and workflow orchestration. The biggest differentiator is the completeness of Rivery’s offering, combining the ease-of-use of a no-code SaaS with the power and flexibility of a custom-coded solution.
Fivetran vs. Matillion – Features
ETL / ELT Tool
Based on server hours
Based on rows
Based on executions and data size
Cloud native and fully managed
Connect to any API source
Built-in workflow orchestration
Built-in data transformation
4.4 / 5.0
4.2 / 5.0
4.8 / 5.0
Fivetran vs. Matillion – Product
Matillion provides two products: Matillion ETL, which is their full-featured ETL offering as well as Matillion Data Loader, which is a lighter weight offering.
Matillion supports 100+ data connectors and users can make new connector requests, but new connectors can only be built exclusively by the Matillion team. In terms of targets/destinations, Matillion Data Loader supports Amazon Redshift, Snowflake, Google BigQuery, and Delta Lake on Databricks for batch pipelines. In addition, Data Loader supports Amazon S3 and Azure Blob, but only for CDC pipelines.
Fivetran is an ETL (extraction and load) tool, where the transformation component of the ETL/ELT process is done by an embedded version of dbt Core.
Fivetran provides 150+ native connectors with support for all main databases and data warehouses as targets. Custom connectors are possible with Fivetran, but they must be built via Cloud Functions, which are then called by Fivetran. These Cloud Functions come at an additional cost to run and need to be maintained.
Building and maintaining custom connectors in-house and outside of Fivetran can be done to handle custom data needs outside of Fivetran’s capabilities.
Rivery’s SaaS offering provides several data stack products in a single platform. This includes ELT (data ingestion) with over 200+ fully-managed connectors, advanced workflow orchestration, data operations (full support for the development lifecycle), reverse ETL (data activation), and full Python support.
Data Connectors: Fivetran vs. Matillion
When comparing Fivetran vs. Matillion, we find that Fivetran offers many options with 300+ pre-built connectors and 24 destination options in the form of data warehouses, databases, and data lakes. Using line and cloud function connectors allows developers to create and implement custom ones using the support of multiple programming languages, including Python, Java, Go, and others. Fivetran’s fully managed connectors also include Oracle and DB2 HVA connectors reserved for those with higher-tiered plans.
Matillion offers its users over 100 connectors for the ETL product, while the Data Loader product has more than 41 connectors. Source connectors feature various SaaS applications, streaming services, cloud storage, and databases. And while the connectors are developed in-house, only a handful support CDC and schema drift features. Matillion also allows users to develop their own connectors using the no-code UI.
Rivery has an extensive selection of more than 200 native connectors, offering users many possibilities to create custom connectors. They are committed to enhancing workflow efficiency by consistently expanding the connector library with new additions. The connectors allow full data extraction and sync frequency control and offer integration with any data source (accounting and finance, analytics, databases, marketing). Users can extract data using Change Data Capture and standard SQL extraction throughout the data integration process.
CDC: Fivetran vs. Matillion
Data movement and replication in Fivetran is done through log-based Change Data Capture (CDC), allowing real-time analytics. Users will find that the Fivetran CDC pipelines are agentless, meaning easier maintenance and reduced complexity.
The connectors are easy to set up, and their CDC-optimized technology enables efficient and high-volume data movement. You’ll find that the pre-built connectors simplify the data centralization and transformation task from various SaaS applications and on-premises data sources to different cloud destinations.
When viewing Fivetran vs. Matillion, the latter has an agent-based CDC implementation, meaning administrative access is required for the source and destination systems in order to function. You will be required to install one agent per pipeline. However, keep in mind that installing agents and requirements for accessing the Amazon Secrets Manager application demands that users have knowledge of cloud architecture and system administration.
Rivery’s CDC solution offers users flexibility, stability, and I/O support. The multichannel compatibility contributes to reduced time for data extraction from different sources and barely any delays in switching between connectors. This, in turn, minimizes the danger of overburdening the system. You can perform automatic syncing of the database and cloud data warehouse by minimizing resource consumption and maximizing resource efficiency. This will offer users fast data access.
Schema Changes: Fivetran vs. Matillion
Using dbt Core transformations, Fivetran automates the ETL pipelines where data changes are tracked through a fully automated and lossless managed process. The automated schema changes require no manual configuration or reconfiguration. However, since not all sources support pre-built models, you may need to learn SQL or contact their support team for assistance.
Matillion’s users can implement common schema changes like format changes, renaming of columns, and conversion of data type by implementing transformation and data integration. Users define the changes by using SQL scripts and transformation elements to schedule the ETL process. Although Matillion offers various schema change options when comparing Fivetran vs. Matillion, we can say it falls slightly behind in managing the schema changes as Fivetran.
Rivery automatically detects any alterations and updates the schema to align with the new and updated structure. It recognizes and manages the schema modifications through data extraction and transformation from a diverse number of sources. Perform field-level mapping to identify data loading and transformation into the destination source by connecting to cloud storage, NoSQL and SQL databases, APIs, and other data sources.
Performance and Scalability
Data engineers need to understand performance and scalability levels to ensure they have a reliable tool that can handle large amounts of information. Matillion’s serverless architecture allows optimal utilization of resources and, at the same time, minimizes operational overheads. Its cloud-native solutions are built on AWS infrastructure, meaning that it can offer easy horizontal scaling by adding more elements when necessary.
When comparing Fivetran vs. Matillion, Fivetran also has a serverless architecture where demand-based automatic scaling provides high performance in peak loads. By using the connector-based approach, organizations don’t need to worry about limited infrastructures when expanding data pipelines.
Both Matillion and Fivetran offer incremental load support. Parallel processing is available for Matillion users with limited CDC, while Fivetran has limited parallel processing but offers full CDC support.
Ease-of-Use & Collaboration Comparison Table
|User Interface||Graphical UI||Web-Based UI||Command-line interface|
|Role-based Access Control||Yes||Yes||Yes|
|Customization||Limited||Full Support||Full Support|
Fivetran vs. Matillion – Pricing
Matillion’s pricing uses credits and is based on virtual core (vCore) per hour usage. One credit = 1 hour of vCore usage. Credits range between $2.00 and $2.70 an hour. This is in addition to the cost of running the servers themselves on your cloud provider (AWS, Azure or GCP).
Fivetran’s pricing is consumption based. Their pricing model is controlled by monthly active rows (MAR), which is the number of unique primary keys moved from source to target within one calendar month. For some use cases this can make Fivetran’s pricing difficult to estimate.
Fivetran offers a 14-day free trial for new users.
Rivery provides both, on-demand pay-as-you-go pricing, and annual contracts. Both pay-as-you-go and annual pricing are available for all plans.
Pricing is based on a credit system, which starts at $0.75 per credit. Database source pricing is based on total volume of data transferred. App and API-source pricing is based on the number of pipeline executions.
Rivery doesn’t charge by the number of databases or tables you’re loading into your data warehouse, nor does it charge by the number of rows or sync frequency.
All Rivery pricing plans include:
- Access to all 200+ fully-managed connectors
- Unlimited users
- Runtime environments
- Reverse ETL
- Change Data Capture
Architecture: Fivetran vs. Matillion
Ficetran’s zero-maintenance, fully managed approach is complemented by the Extract-Load process, where no transformation occurs. The raw data is simply extracted and loaded from the source into the target system. This contributes to the seamless connectivity between sources. Primarily designed for cloud environments, the hybrid connectors feature enables on-premises systems’ support.
Built on Amazon Web Services (AWS), Matillion leverages cloud platforms, offering flexibility and scalability. You can use Apache Parquet file formats to help with storing and retrieving big datasets or use Glue Catalogs, S3 buckets, Microsoft Azure, or GCP. The ETL approach that Matillion uses loads raw data into the target system and transforms it using modern cloud-based databases.
Built on a multitenant architecture, Rivery offers clients the perk of having separate and invisible databases for each client. This means moving data between separate data warehouses based on given privileges and permissions. This will require the use of a customer-specific Tenant ID, a unique identifier used by each client allowing access and privilege based on user roles. Rivery’s extensive use of the AWS security features helps monitor access and activity, as well as spot anomalies and improve responsiveness.
Deployment Options: Fivetran vs. Matillion
Offered exclusively as a fully-on SaaS solution, clients don’t need to worry about managing underlying resources. They can use the benefits of the automatic updates and seamless scaling capabilities of the managed service model. However, you should be aware that Fivetran is not open source and offers no self-hosting options. Data engineers can concentrate on their core tasks, while Fivetran simplifies the data integration processes by handling all infrastructure management aspects.
Users can select one of the two deployment options offered: self-hosted or SaaS. While Matillion supports self-hosting deployment on platforms like GCP, Azure, and AWS, keep in mind that on-premises installations are not supported. Users opting for SaaS deployment can expect automatic updates, scalability, and ease of use. This is due to the option of accessing the platform through a web browser.
Running exclusively on AWS means users can deploy the software as a Deployment package consisting of Connections, Rivers, River Groups, and Variables. This means modifying the software based on needs or, if necessary, reverting it to the original form, which can be done only by Admins. Deploying test environments and achieving unique objectives is possible with Rivery’s Environment.
Fivetran vs. Matillion – Support
Matillion provides support through an online ticketing system. They do not provide any training services as part of their support offerings.
Matillion’s G2 user rating for quality of support is 8.1/10 with an industry average of 8.5/10 for ETL tools on G2.
Fivetran offers support through in-platform support tickets. They do not provide training services as part of their support offerings.
Fivetran’s G2 user rating for quality of support is 7.8/10 with an industry average of 8.5/10 for ETL tools on G2.
Rivery offers 24/7 global support as well as onboarding services and dedicated customer success services, which vary by plan. Support is engineering led and year over year, Rivery has been awarded “Easiest to do Business with” by G2 in the category of ETL tools.
Rivery’s G2 user rating for quality of support is 9.9/10, the highest in its category with an industry average of 8.5/10 for ETL tools on G2.
Fivetran vs. Matillion – Summary
Matillion ETL is basically sold as software, and is not a SaaS offering. This is suitable for organizations that require running all of their data pipelines on their own servers, and they will need a team to manage the servers software upgrades.
If you have a large data team, conduct on-premise deployments, and are looking for advanced transformation capabilities, Matillion may be for you. You must take into consideration that you’ll be responsible for managing the infrastructure running Matillion.
Key benefits when evaluating Matillion include:
- Matillion licenses are relatively cheap and their compute runtime is based on cloud computing price.
- Strong transformation capabilities
Limitations of Matillion include:
- Host infrastructure management needs to be handled as your data pipelines grow.
- Cannot handle incremental updates automatically
- Database supportability
Fivetran is a great option for teams looking for an easy-to-use data integration tool. However, you’ll have to pair Fivetran with other tools to implement an end-to-end ETL/ELT solution. For example, you’ll need to leverage different tools like, dbt for transformation, Airflow for orchestration, and Hightouch or Census for reverse ETL.
Key benefits when evaluating Fivetran include:
- Ease of connection
- ERD/normalized schemas for each SaaS solution
- Embedded dbt Core with custom dbt models
Limitations of Fivetran as an ETL/ELT tool include:
- No proprietary transformation capabilities
- Custom connectors need to be built and maintained via Cloud Functions
- No developer capability to allow you to extend Fivetran capabilities
- No reverse ETL capabilities
- No orchestration capabilities
Fivetran vs. Matillion – A Third Choice
Looking for alternatives? A third choice to consider is Rivery.
Rivery’s fully-managed SaaS offering addresses the entire data flow journey and development lifecycle by providing a unified, cloud-native solution for both technical and non-technical users.
This offering includes ELT (data ingestion) with over 200+ fully-managed connectors, advanced workflow orchestration and scheduling, data operations, reverse ETL (data activation), and full Python support.
Rivery provides global support, onboarding services, and dedicated customer success services, which vary by plan. Rivery’s G2 user rating for quality of support is 9.9/10, the highest in its category with an industry average of 8.5/10 for ETL tools on G2.
Rivery pricing includes pay-as-you-go and annual contracts, both of which are available for all plans. Pricing is based on a credit system, which starts at $0.75 per credit. Rivery doesn’t charge by the number of databases or tables you’re loading into your data warehouse, nor does it charge by the number of rows or sync frequency.
When evaluating Rivery in comparison to other ETL/ELT tools, the biggest differentiator of Rivery is the completeness of their offering, combining the ease-of-use of a no-code SaaS with the power and flexibility of a custom-coded solution.
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