matt's blog


Idempotence is one of those terms that frequently gets tossed around, but can be tricky to understand:

In mathematics and software engineering, idempotence is a property of operations that specifies no matter how many times you execute them, you achieve the same result.

In data, that applies to the operations we perform—extract, transform, & load. Idempotent data pipelines return consistent results, regardless of how many times they’re run or if they fail mid-run.

Let’s say using an Airflow DAG, you pull data from a daily updating API and write it to S3. Each day, new data is present in the API. You take that new data and drop it in S3 with the existing data.

Sounds simple, right? Building an idempotent pipeline means asking:

Idempotence is a simple concept, but can quickly become confusing especially if dealing with complex merge operations, like UPSERT or other data engineering patterns, like snapshot tables.

Things get a bit more complicated when considering incremental pipelines. Now, this isn’t exactly hard, it’s just more confusing. Thankfully, dbt and similar tools have pre-built patterns that make incremental models a breeze.

So, here are some tips to make sure your pipelines are idempotent & adhere to best practices:

The easiest way to ensure these properties hold is to seek out solutions (data integration, transformation, and orchestration tools) that come out-of-the-box with incrementality, automated retry logic, and patterns like UPSERT. Trust me when I say these are solved problems and your team will save lots of time and energy by reusing frameworks that have already been built and tested.

If you do choose to build your own system (or it’s necessary for your top-secret pipelines), stick to data modeling best practices, then build in smart processing and intelligent incremental pipelines to ensure robust, idempotent operations.

#data #opinion