With more data sources than ever, you've likely already encountered two of the leading ETL solutions -- Fivetran and Matillion.
In this comparison, we'll walk you through the pros and cons of the two platforms.
We'll outline the functionality and the pricing models for each platform and even offer a simple framework to understand when to use each platform for data management.
The two most common use cases for data integration tools are 1) analytics and 2) automation.
Data integration solutions make it simple to extract data from APIs, databases, and files to then load the data into your data warehouse for business intelligence.
When using data for analytics use cases, data engineers leverage an ETL tool to load data from SaaS applications into Snowflake, Google BigQuery, Amazon Redshift, PostgreSQL, or SQL Server. From there, teams can build dashboards for better corporate decision-making.
On the other hand, automation use cases involve replacing manual tasks with real-time, automated workflows that sync data from one data source to another business application in a low-code or no-code manner.
Historically, ETL was difficult. You would need to hire data engineers, write code, and deploy a solution on-premises. Only then, could your team centralize the various data sources from across your enterprise into an analytics environment. There were early data integration platforms like Talend and Informatica that helped, but they weren't intuitive, had to be deployed on-premises, and the pricing was entirely tailored to enterprises.
In 2022, things have changed. No-code and low-code ETL and ELT tools make it simple to orchestrate workflows that move data from APIs, SaaS applications, databases, and files to your cloud data warehouse with minimal overhead. Instead of spending countless hours writing code, data teams can now use pre-built connectors to extract and load data for analytics and automation.
It doesn't matter if you're a small business building dashboards, or a large enterprise working with big data, navigating HIPAA, implementing data governance best practices, and training machine learning models. Everything starts with finding a simple way to ETL data into your data warehouse or data lake.
So, how does your data team benefit from an ETL tool?
You save the headaches and pain of building data pipelines (goodbye python, hello SQL), and instead, tap into pre-built connectors to extract data from hundreds of sources across your enterprise.
Data from collaboration tools (Microsoft 365, Asana, ClickUp), CRM systems (Salesforce, HubSpot), ERP platforms (NetSuite, Oracle), and email service providers (MailChimp, ActiveCampaign) can all be centralized without writing a single line of code.
Does your team love to code?
Great! Spend your time writing SQL, building dashboards, running machine learning models, and implementing best-in-class data governance frameworks. With ETL tools, you can free up your team to build data products instead of re-inventing the same data pipeline that every other business intelligence team is already leveraging.
ETL platforms like Fivetran and Matillion help business intelligence teams in three ways:
1. Self-service data extraction. With hundreds of pre-built data connectors to common SaaS applications and databases, both platforms make data replication simple. No-code ETL tools are the fastest way to centralize data within your Modern Data Stack. 2. Ready-to-query schemas for orchestration and data transformation. By syncing data into the warehouse, no-code solutions can be integrated with open source orchestration and transformation tools like Airflow and DBT to build data models, execute DAGs, and orchestrate complex pipelines. 3. Low maintenance data pipelines. Leveraging an out-of-the-box solution allows your data engineers to analyze data without having to worry about rate limits, errors, hardware failures, and scaling issues. Vendors like Fivetran and Matillion offer a simple, low-maintenance solution.
Now, let's first dig deeper into Fivetran.
Fivetran was founded in 2012 as an ELT tool for hyper-growth companies.
Fivetran has a strong reputation for maintenance and support for databases (change data capture) and the largest business applications. When teams get started, they can't go wrong with Fivetran for scalable data pipelines.
Supporting a native integration with DBT, Fivetran can help turn raw data into insights with native transformation. For connectors that have complex data models, these off-the-shelf queries can save analytics teams significant work.
Support for enterprise data sources. Oracle, SAP, and Workday are a core focus of Fivetran's business model as they expand into enterprise clients. These connectors are extremely complicated and are difficult to find reliable solutions elsewhere. Fivetran spent $700M to acquire HVR to deepen these capabilities.
Cost is a big downside of the Fivetran platform. With pricing based on Monthly Active Rows, it's easy to end up in a scenario where a data source syncing large data volumes can cost a significant amount of money.
Not only is the product expensive, but costs are very difficult to predict and forecast, adding additional stress to data teams, and additional scrutiny from finance stakeholders.
Fivetran takes months or years to build new connectors. And in many scenarios, they do not plan to build connectors at all. In a scenario where you need a connector and Fivetran doesn't support it out-of-the-box, you aren't offered great options. Fivetran asks clients to script their own solutions using cloud functions. (By the way, this is a common scenario where clients reach out to Portable for help).
Matillion was founded in 2010 as a data replication and transformation tool for enterprises.
Matillion differentiates from most ETL vendors based on their native transformation capabilities. With strong capabilities for GUI-based data transformation, Matillion can provide enterprises with a simple way to organize queries and relationships between those queries for data modeling purposes.
Another unique aspect of the Matillion platform is the product's ability to be deployed on-premises (which can be particularly helpful in regulated industries or when data transfers across boarders come into play - i.e. GDPR considerations). Most other ETL solutions fall into one of two buckets: 1) cloud-hosted SaaS, or 2) open-source code (where code can be leveraged, but the product isn't as accessible to be deployed on-premises).
Because data loading capabilities and transformation are both available within the Matillion platform, in some scenarios, it can make it simpler to apply data governance best practices vs. an approach where data replication and transformation are decoupled.
Matillion has a limited connector catalog relative to the other best-in-class ETL solutions. In scenarios where long-tail connectors aren't available in Matillion, it's a great reason for users to leverage Portable find a simple, secure, solution.
The user experience of Matillion centers around the GUI based transformation capabilities. It can take longer to get up-to-speed and can be intimidating for new users trying to stand up their data stack in minutes instead of days or months.
Because Matillion does more than just data replication, it can lead to Matillion having fewer partnerships for other capability sets relative to other ETL tools. For teams that want a modular data stack, Matillion can lead to vendor lock-in because it's more of a platform solution.
Now that we've outlined the pros and cons of the two platforms, let's outline Fivetran as a Matillion alternative, and Matillion as a Fivetran alternative.
It is important to dig into the true capabilities of the platforms we are considering. Let's dive into the features, functionality and pricing of the two platforms.
One of the most important criteria for selecting an ETL tool is whether or not the product supports the data sources you need.
Most vendors don't build many new data sources each year, so when you consider the offering, you're really purchasing access to the connectors they already have in their catalog. Breadth of connectors is a strong proxy for a vendor's ability to help your analytics team centralize data.
Fivetran's connector catalog is just over 160 data sources.
Fivetran's connectors are typically the largest databases (similar to the catalogs of Stitch Data and Airbyte), file-based sources, and business applications that are helpful when your team is first standing up your data stack.
Matillion's catalog includes 110+ data sources.
Because the company offers two separate solutions (Matillion Data Loader and Matillion ETL), the ETL solution can include connectors that are not available in the Data Loader product.
When your team needs a new connector, you NEED the connector.
It's important to understand how both data integration platforms will help in these scenarios. Do they ask you to write code? To maintain the connector? To fix things when they break?
Fivetran asks clients to use cloud functions to build custom connectors and maintain them when things break.
As part of the development process, users need to read API documentation, deploy a cloud function, make sure things work, and then maintain the pipeline if things ever break. This can be a significant amount of work, and is a common scenario when data teams reach out to Portable for help.
Matillion does not support long-tail data sources, but users can use the GUI to build basic API integrations.
To build a custom integration with Matillion, the user needs to read the API documentation, configure the setup in the Matillion user interface and then support the integration as issues arise.
Data integrations are living, breathing organisms. They evolve, they break, and they cause chaos with your queries and dashboards when they do.
It's critical to understand how both ETL vendors will support you when things go wrong, and what functionality each platform has in place for alerting, monitoring, and connector maintenance.
Fivetran offers a ticketing system for clients to flag issues when things go wrong.
Because Fivetran has a large client base, issues are typically picked up quickly, but support can feel impersonal at times.
Matillion focuses on enterprises, can be deployed in your own cloud environment (AWS, GCP, Azure) and offers SLAs and a support portal to clients.
Because the platform also can include transformation, governance and on-premises deployments, there can be more moving parts to troubleshoot and maintain when leveraging Matillion relative to most other ETL solutions.
Finally, let’s compare the pricing of Fivetran vs. Matillion. There are both similarities and differences to be aware of.
Fivetran charges on Monthly Active Rows for data pipelines.
Matillion Data Loader is priced on rows loaded per month.
Now that we've outlined what each brand offers, let's quickly recap the takeaways.
Choosing an ETL solution is an important decision that you need to make based on your own specific needs.
We've outlined the pros and cons of both Fivetran and Matillion to help frame out the scenarios in which each solution makes sense.
At Portable, we focus our efforts on a customer-first culture, a try-before-you-buy business model, and hands on support when things go wrong.
Portable is the first ELT platform to build connectors on-demand for data teams. We believe companies should have data from every business application at their fingertips - all with no code. We have more no-code ELT connectors than both Fivetran and Matillion (250+ and counting).