In 2023, data engineers are automating common data pipelines by using ETL tools to replicate data from disparate business applications into their cloud data warehouse for analytics.
With more data sources than ever, you've likely already encountered two of the leading ETL solutions -- Fivetran and Segment.
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.
If you're reading this guide, you have likely already identified a use case for data, and now you're wondering - How do I get data integrated from my business applications into my data warehouse or data lake for analytics?
There are few solutions as well known as Fivetran and Segment for easy-to-use no-code 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 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 Segment help business intelligence teams in three ways:
Self-service data extraction. With hundreds of pre-built data connectors to common SaaS applications and databases, both platforms make data replication simple.
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.
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 Segment offer a simple, low-maintenance solution.
Now, let's first dig deeper into Fivetran.
Fivetran is an ELT tool for hyper-growth companies.
A Fivetran subscription includes several capabilities, including:
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).
Segment is a data integration solution for customer data.
A Segment subscription includes several capabilities, including:
Real-time routing of data to advertising and marketing destinations (similar to Reverse ETL capabilities)
Broad suite of customer data related sources and destinations
Native data collection to create data from website and mobile app events
Built for marketers instead of data teams
Limited support for non-marketing related sources
Data from sources is typically limited to customer data
Now that we've outlined the pros and cons of the two platforms, let's analyze Fivetran as a Segment alternative, and Segment 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.
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. For example, Amazon Ads, Oracle PeopleSoft, and Salesforce Marketing Cloud.
Segment offers 75+ prebuilt connectors to data sources, and hundreds of destinations. Because Segment is a CDP built for marketers, the prebuilt connectors are primarily focused on sources that manage and store customer data.
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.
Segment offers a simple way to create data from your website or mobile app; however, the platform does not offer a simple solution to extract data from custom applications that are not supported out-of-the-box.
Let’s now compare the pricing of Fivetran vs. Segment. There are both similarities and differences to be aware of.
Fivetran charges on Monthly Active Rows for data pipelines.
Segment charges primarily on the number of visitors per month.
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.
Segment is a self-service product built for engineering teams and marketing teams. Support is mostly self-service via documentation and help center articles unless you are on the enterprise tier.
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 Segment 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.