The main difference between Portable and Fivetran is that Portable is exclusively focused on long-tail, custom ETL connectors that are personally built by the CEO within days. Meanwhile, Fivetran is more focused on serving well-known, mainstream data sources. Fivetran offers a consumption based pricing model, while Portable DOES NOT CHARGE BASED ON VOLUMES.
In this comparison, you'll understand the pros, cons, functionality and pricing models for each ETL solution -- plus you'll get a simple framework to understand when to use each platform based on your data integration strategy.
The two most common use cases for data integration are:
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, 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.
You're probably wondering - How do I get data integrated from my business applications into my data warehouse or data lake for analytics?
We'll evaluate Portable and Fivetran as well as other easy-to-use no-code data connectors.
The short answer? Every business intelligence team.
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 2023, things have changed. No-code and low-code ETL data integration 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.
Data engineers are now easily able to automate common data pipelines by using ETL tools to replicate data from disparate business applications into their cloud data warehouse for analytics.
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.
You save the headaches and pain of building your own 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.
Your team can 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 Portable and Fivetran 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. 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 Portable and Fivetran offer a simple, low maintenance solution.
Portable is an ELT tool purpose-built for the long-tail of business applications.
250+ no-code connectors that you can't find with other ELT vendors. Portable exclusively focuses on the long-tail of business applications that aren't supported by other data integration tools.
Free connector development. Unlike other ELT vendors, Portable builds connectors on-demand for clients. In addition to 250+ off-the-shelf connectors, Portable will build new connectors on-demand as you need them.
Hands on technical support. APIs break and issues arise. Portable provides a turnkey solution for analytics teams to move data without worrying about issues when things go wrong. The Portable team is on-call when things break, so you don't have to worry.
Pricing is simple and straightforward. Instead of pricing with credits, rows, or custom mechanisms, Portable's pricing model is simple. Flat rate pricing per data flow with unlimited data volumes included. Stop worrying about credits and data volumes, and get back to writing SQL and building dashboards.
And the best part is that Portable DOES NOT CHARGE ON DATA VOLUMES.
The price you sign up for is the price you'll pay. With other ELT tools, costs add up quickly, and are almost impossible to forecast.
Portable doesn't offer the largest data sources most teams need when they get started with analytics.
Portable doesn't focus on databases as sources or the biggest business applications (Salesforce, QuickBooks, etc.).
Instead, as data teams grow, they get more requests for bespoke integrations - HR platforms, eCommerce tools, marketing systems, etc. - and Portable provides a unique catalog of long-tail integrations for these specific scenarios.
Fivetran was founded in 2012 as an ELT tool for hyper-growth companies.
Fivetran has many competitors, but they are among the strongest in brand 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.
With 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.
Fivetran offers strong technical 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 are prices high, but they 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 for clients to reach to Portable for help).
Now that we've outlined the pros and cons of the two platforms, let's outline Portable as a Fivetran alternative, and Fivetran as a Portable 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.
Portable provides access to 250+ data sources out-of-the-box. With a catalog larger than most other ETL tools, it's easy to connect the sources you need to your analytics environment of choice.
Uniquely, Portable focuses on long-tail business applications. This means that the connectors in Portable's catalog are hard-to-find and are great for augmenting your primary ELT vendor for the no-code connectors you need as your responsibilities expand.
Fivetran's connector catalog is just over 160 data sources.
These connectors are typically the largest databases, file-based sources, and business applications that are helpful when your team is first standing up your data stack.
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?
Portable specializes in developing new ELT connectors on-demand for clients.
It's simple to request a new data source, and most connectors are developed in hours or days. Development is free, and you can even try the connectors without even entering a credit card. If there are ever issues, the Portable team is on-call and will get things fixed as quickly as possible.
For custom connectors, Fivetran asks clients to use cloud functions to build their own connectors and maintain them when things break.
As part of the development process, users need to read API documentation, deploy a serverless cloud function, make sure things work, and then maintain the serverless cloud function 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.
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.
Portable's product includes automated retries, error handling, and rate limit management as well as email Slack notifications. If a data flow fails, the Portable support team is directly looped in to troubleshoot the issue and offer the lowest effort solution for your to get your data back up and running quickly.
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.
Finally, let’s compare the pricing of Portable vs. Fivetran. There are both similarities and differences to be aware of.
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 Portable and Fivetran 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.
There's no downside to exploring our connector catalog, or even requesting the connector that's at the top of your backlog.