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Segment is a popular customer data platform (CDP) that enables users to collect and manage customer data in different formats and from different sources. The platform hosts tools to transform, migrate, and manage data with third-party destinations, including data warehouses.

Segment is a powerful tool that can appear complex. By connecting Segment to an ETL tool, data can be manipulated and effectively analyzed without the added stress of generating and maintaining ETL scripts and large external tech stacks.

In this guide, we'll cover some of the best Segment ETL tools that can guide data analysis for your business. Here are the key things to know when looking for Segment ETL tools:

  • While Segment includes many of its own data transformation and integration capabilities, more advanced and automated capabilities are made possible through third-party ETL tools, like Integrate.io
  • Segment is considered a powerful but incredibly complex CDP for data management; ETL tools with low-code/no-code interfaces can help to simplify these data analytics and integration projects.
  • A guided customer success or support workflow, particularly during onboarding, is a great thing to look for in an ETL tool that integrates with Segment.
  • Segment ETL tools tend to work with a range of data warehouse and BI tool destinations, whereas Segment by itself is somewhat limited.
  • Because Segment is primarily a platform for managing customer data, it's a good idea to look for Segment ETL tools that include established connectors for CRMs, ERPs, and other data storage and analytics platforms that focus on demographic data.

What Are Segment ETL Tools?

Segment ETL tools are extract, transform, and load (ETL) platforms that offer compatible connectors, data sources, and data destinations for Segment CDP integrations. Segment includes many native data transformation and integration features, so for a Segment ETL tool to truly deliver value, it must include more advanced automation and scalability workflows, low-code/no-code connectors to more varied data destinations, and strong customer support teams and learning resources.

How to Evaluate Segment ETL Tools

Here are the key things to consider when evaluating segment ETL tools:

Features

Segment offers many integration and data warehousing features that make it a useful tool all on its own. However, the right ETL tool can supplement and optimize these existing features, giving users more control and visibility over their customer data, related analyses, and workflows.

When selecting an ETL tool for your Segment data workloads, look for a platform that offers support for a range of relevant data destinations, including both data warehouses and BI tools; API and file ingestion capabilities, implementation and documentation resources, and low-code/no-code automation workflows.

Usability

The reason why many Segment users opt to tack on a third-party ETL tool is that Segment's built-in capabilities are not always the easiest to use from the outset. Segment does not include many low-code/no-code capabilities and its customer support is fairly hands-off; in general, customers comment on the difficulty of initial setup and ongoing maintenance.

To increase the usability and accessibility of Segment data, look for ETL tools that have a low-code/no-code and drag-and-drop interface, documentation that uses layman's terms, prebuilt connectors, and customer support teams with high levels of availability and expertise.

Pricing

Segment itself is a fairly affordable tool, with a starting free plan that includes access to more than 450 integrations, one data warehouse destination, two sources, and 500,000 reverse ETL records. With that in mind, it makes sense that Segment customers would be interested in ETL tools that balance affordability with a deep collection of useful features.

To keep things within budget, determine your third-party ETL tool budget ahead of time and research the range of price points among your top choices; in many cases, you will be able to find a low-cost — or even a free, open-source option — that works for your needs.

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1. Integrate.io

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Rating: 4.3/5 (G2)

Key Features:

Integrate.io is a top data integration and ETL solutions provider that gives customers a drag-and-drop user interface, prebuilt connectors for a range of destinations, and extensive customer support.

Despite these many features that emphasize ease of use, Integrate.io does not skimp on robust ETL and auxiliary features. For example, Integrate.io customers particularly enjoy the platform's 60-second data replication capabilities, data observability monitoring and alerts, and on-demand detailed data warehouse insights with data price optimization and other recommendations.

Integrate.io is priced per product, with the ETL and Reverse ETL product starting at $15,000 per year. A free 14-day trial is also available.

Pros :

  • Modular access to a variety of data management and integration tools, including for CDC and data observability needs.
  • All plans include access to unlimited packages, transfers, and users.
  • Highly rated and responsible customer service team, with at least 30 days of tailored onboarding and a dedicated solutions engineer.

Cons:

  • Somewhat limited connector- and data-source-specific documentation.
  • The ETL and Reverse ETL products can be expensive, particularly for smaller teams and budgets.

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2. Stitch

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Rating: 4.5/5 (G2)

Key Features:

  • Standard and Stitch-certified data sources
  • Open-source data extraction framework
  • Free historical data replication
  • Extensible REST API
  • Developer interfaces and reports

Stitch, now a part of Talend, is a popular cloud platform for automated data pipeline and ETL management. Segment is one of Stitch's certified data source options that can be integrated with more than 10 data warehouse and storage options, including some of the smaller players in that space. Users frequently choose Stitch for its security and compliance features, its data transformation quality, its extensibility, and its strategy of structuring data to be compatible with leading data analysis and BI tools.

Stitch's Standard plan is available for $100 billed monthly or $1,000 billed annually for 5 million rows. A 14-day or two-month free trial is also available to users, depending on when you subscribe to the platform.

Pros :

  • A variety of security and compliance standards and safeguards are built into the platform.
  • Pricing is considered fairly reasonable, at least for smaller volumes of data.

Cons:

  • Stitch's starting Standard plan includes many limitations, including on user count.
  • Some users believe that connectors are buggy and need to be more regularly updated; in general, users have concerns about Stitch's customer service and responsiveness.

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3. Panoply

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Rating: 4.5/5 (G2)

Key Features:

  • SQL workbench
  • Native graphs, charts, and visualizations
  • Flex Connector and compatibility with other third-party ETL tools
  • Multi-source data syncing
  • Automated data type detection

Panoply is a unique player in this space as it plays two different roles: It is primarily a modern data warehouse environment, but with its Flex Connector and customizations, it also functions as an ETL tool. Its role as a data-warehouse-first environment makes it compatible with many other ETL competitors, including Stitch; so whether you're moving to or from Panoply or wanting to use it as part of a multi-tool environment, chances are it will be compatible with your business data management goals.

Segment is one of Panoply's Snap Connectors, meaning it can be set up in a matter of minutes. Panoply also supports Segment data syncing with multiple Panoply sources, meaning users can combine Segment data with more than 200 other data source options without using any code.

Panoply's plans start at $389 billed monthly or $299 per month, billed annually. This price point works for up to 10 million rows per month.

Pros :

  • Panoply itself is a managed ELT and cloud data warehouse, which takes out some of the middlemen tools and costs for ELT integration projects.
  • Many users have favorable reviews of customer support, especially some of the changes the vendor has made in recent months.

Cons:

  • Users are fairly limited to BigQuery for their data destinations due to the approach Panoply takes to data warehousing.
  • While Panoply offers no-code features that are mostly easy to use, users can easily overload data they don't need and surpass monthly plan limits.

Integrate.io: Get Started With Our Segment ETL Solution

Integrate.io is a great ETL solution for Segment users because while Segment users often have concerns about the tool's accessibility and complex features, Integrate.io customers consistently praise that platform and vendor for its accessible features and responsive and professional customer service resources.

It's significant that while many other tools in this space require users to do most of the setup and implementation work on their own, Integrate.io users receive access to a professional solutions engineer as soon as they begin engaging with Integrate.io.

Ready to see how it works with Segment ETL projects? Book a demo with Integrate.io today.

Segment ETL FAQs

How Do ETL Tools Work?

An ETL tool, short for Extract, Transform, Load, is a software solution that simplifies the process of gathering data from various sources, reshaping it to meet specific criteria, and then loading it into a centralized database or data warehouse. These tools automate and streamline these operations, making data integration and transformation more efficient.

What Are The Key Features To Look For In a Segment ETL Tool?

Important features in an ETL tool include robust data extraction capabilities, advanced transformation functions (e.g., data cleansing, aggregation, and validation), support for diverse data sources (such as databases, spreadsheets, and web services), scalability for handling large datasets, and effective error handling to maintain data quality.

Can ETL Tools Handle Both Structured and Unstructured Data?

Yes, ETL tools are versatile and capable of managing both structured data, like information stored in databases, and unstructured data, such as text documents, images, or JSON files. This versatility allows them to accommodate a wide range of data types.