Iterable's `email_list_ids` Deprecation: A Migration Guide

Aug 14, 2025 by Benjamin Cohen 59 views

Deprecation of `email_list_ids` in `USER_HISTORY` and Introduction of `LIST_USER` Table

Hey everyone! Let's dive into an important update regarding our Iterable data integration. As you know, we're always striving to keep our data models clean, efficient, and aligned with the latest changes from our sources. In this case, we're tackling a deprecation and a new feature that will impact how we handle user list information.

Understanding the Change: Say Goodbye to `email_list_ids` in `USER_HISTORY`

Our main focus is the email_list_ids column deprecation. As per the announcement in the August 2025 Fivetran connector release, the email_list_ids column is scheduled to be removed from the USER_HISTORY table on October 13, 2025. This is a crucial change to be aware of because if you are anything like us, you are probably using this data in various downstream models and reports. Change is the only constant, and in the world of data, that means adapting to evolving schemas and data structures to maintain data integrity and consistency.

So, why this change? Well, data platforms evolve, and sometimes, the way data is structured needs to change to optimize performance, maintainability, or align with new features. In this case, Iterable is introducing a more streamlined approach to managing user lists, which leads us to the next big thing: the LIST_USER table.

The reason behind removing the email_list_ids column is to make the USER_HISTORY table more focused on user-specific historical data, while the list memberships are handled in a separate, dedicated table. This separation of concerns typically leads to more efficient queries and a clearer data model overall. Imagine trying to find a specific user's history when the table is cluttered with list information. Separating this data makes things much cleaner and faster.

But what does this mean for us? It means we need to adjust our data pipelines and models to accommodate this change. We need to ensure a smooth transition so that our downstream processes continue to function without any hiccups. We will need to find a way to replicate what the email_list_ids column did for us previously. No stress, we've got this! That's where the new LIST_USER table comes in, and it's actually a pretty neat solution. Let's dig deeper into it.

Welcoming the `LIST_USER` Table: A New Home for List Memberships

The good news, guys, is that the data isn't going away; it's just moving to a new address! The LIST_USER table is the new place to find information about which users belong to which lists. This table essentially replicates the data that was previously available in the email_list_ids column of the USER_HISTORY table. Think of it as a dedicated directory for user list memberships, making it easier to query and manage this specific type of information.

The introduction of the LIST_USER table is a strategic move by Iterable to provide a more organized and efficient way to manage user list memberships. By creating a separate table specifically for this purpose, they're enabling more streamlined queries and better performance. This dedicated table allows for faster lookups and reduces the load on the USER_HISTORY table, which can improve overall system performance. It is like having a specific filing cabinet for a particular type of document, rather than mixing everything together. Much easier to find what you need.

So, what kind of information can you expect to find in the LIST_USER table? Well, it will likely include key details such as the user ID, the list ID, and potentially other relevant information like subscription dates or statuses. This table will give us a clear and concise view of user-to-list relationships. This information is crucial for understanding user engagement, targeting campaigns effectively, and personalizing communications. Knowing which users are on which lists allows us to tailor our messaging and deliver more relevant content.

The main benefit of using the LIST_USER table is that it allows us to maintain the same level of functionality we had with the email_list_ids column, but in a more structured and efficient way. We can continue to track user list memberships and use this information in our downstream models and reports. The only difference is that we'll be querying a different table. Plus, with a dedicated table for list memberships, we can expect improved query performance and a more organized data model overall. Who doesn't love that?

Charting Our Course: Two Options for Transition

Okay, so we know the change is coming, and we know about the new table. Now, how do we navigate this transition? As I see it, we have a couple of options on the table:

Option 1: Rip It Out - Remove email_list_ids from USER_HISTORY: This is the most direct approach. We simply remove the column from our models and adjust any downstream processes that rely on it. It's like performing surgery - a clean cut. However, this option could potentially cause disruptions if we don't have a clear understanding of all the places where email_list_ids is used. It's a bit of a scorched-earth approach, and might leave some downstream dependencies stranded.
Option 2: The Diplomatic Approach - Add LIST_USER and Join It In: This approach involves incorporating the LIST_USER table into our data models and joining it with other tables where we currently use email_list_ids. This allows us to maintain the same functionality while adapting to the new data structure. It's more like a gentle transition, ensuring a smoother ride for our data pipelines. In this approach, we would add the LIST_USER table to our data warehouse and then modify our existing models to join the USER_HISTORY table with the LIST_USER table whenever we need to access user list information. This join would effectively replicate the functionality of the email_list_ids column, allowing us to continue using the same logic in our downstream models.

The Verdict: Why Option 2 Seems Like the Winner

In my humble opinion, Option 2 (Adding LIST_USER and joining it in) seems like the better path forward. Why? Because it offers a more seamless transition and minimizes potential disruptions. We know that email_list_ids is used in a few downstream models, so simply removing it could lead to broken pipelines and inaccurate reporting. No one wants that!

By adding the LIST_USER table and joining it where needed, we can effectively replace the functionality of the email_list_ids column without having to rewrite a ton of code or rework our entire data model. It's a more surgical approach, targeting the specific areas that need to be updated while leaving the rest of the system untouched. This approach also gives us the flexibility to gradually migrate our models to use the new table, rather than having to make a sudden and potentially disruptive change.

Think of it as building a bridge instead of blowing up a dam. We're providing a new path for the data to flow, ensuring that everything continues to work smoothly. By choosing this option, we're prioritizing stability and minimizing risk, which are always good things in the world of data.

Next Steps: Let's Get This Done!

Okay, so we've got a plan. Now it's time to put it into action. Here are the next steps I propose:

Inventory Check: First, we need to conduct a thorough inventory of all the models and processes that currently use the email_list_ids column. This will give us a clear picture of the scope of the change and help us prioritize our work. We need to identify all the places where the email_list_ids column is used so that we can ensure that we update them to use the LIST_USER table instead. This is like taking stock of our supplies before embarking on a journey – we need to know what we have and what we need.
Model Migration: Next, we'll begin migrating our models to incorporate the LIST_USER table. This will involve adding the table to our data warehouse and updating our queries to join it with the USER_HISTORY table. We will need to carefully test these changes to ensure that the data is being joined correctly and that our models are producing the expected results. Think of it as upgrading the engine of a car while still keeping the wheels on the road. We want to improve performance without causing a breakdown.
Testing, Testing, 1, 2, 3: Rigorous testing is crucial to ensure that our changes are working as expected. We'll need to validate the data and ensure that our reports and dashboards are accurate. Testing should include unit tests to verify individual components and integration tests to ensure that the entire system works together seamlessly. This is like a dress rehearsal before the big show – we want to catch any issues before they impact our users.
Monitoring and Maintenance: Once the changes are implemented, we'll need to monitor our data pipelines to ensure they're running smoothly. We'll also need to be prepared to address any issues that may arise. Monitoring will help us identify any performance bottlenecks or data quality issues, and maintenance will ensure that our data models remain accurate and up-to-date. This is like regularly servicing a car – we want to keep it running smoothly for the long haul.

Let's Collaborate and Conquer!

This is a team effort, folks, and I'm confident that we can navigate this change successfully. Your input and expertise are invaluable, so please don't hesitate to share your thoughts, ideas, and concerns. By working together, we can ensure a smooth transition and continue to deliver accurate and reliable data insights.

Let's discuss this further and create a detailed plan of action. I'm excited to see how we can leverage the LIST_USER table to enhance our data models and improve our overall data capabilities. Let's go team!