Flatfile vs. ETL
If you’re comparing Flatfile to traditional ETL tools, you’re likely managing complex data flows that are often unpredictable. But these tools solve fundamentally different problems within that ecosystem. Here’s where Flatfile fits in the data flow—and where it doesn’t—so you can choose the right solution for the job.
When your data is already structured, use ETL
ETL tools move consistently structured and semi-structured data between systems. Whether syncing databases, aggregating records into a warehouse, or transforming API payloads on a schedule, ETLs perform reliably when data fits predictable patterns. In these cases, ETLs can apply defined transformations to keep data aligned and synced across systems.
When your data is unpredictable, use Flatfile
Flatfile is built for the data intake layer, where files arrive from customers, partners, or other third parties and rarely follow a consistent schema. Flatfile solves the challenge of intake workflows where data structure is unpredictable and standardization is critical.
Where the tools fit in the data flow
ETL tools are powerful once data is consistent. Flatfile gets you there.
High-variance initial data ingestion
Import → Prepare → Approve
Flatfile readies data for your destination schema, with members of your team and Flatfile’s AI agents collaborating on data preparation and transformation.
System-to-system transfers for known schemas
Extract→ Transform → Load
ETL tools automate transformation of known schemas using predefined rules—ideal for recurring jobs when structure is known.
Where Flatfile and ETLs fit in the data flow
Customers uploading CSVs to configure their account? Partners sending spreadsheets during onboarding? Legacy data from past systems or inconsistent exports?
Flatfile addresses these early-stage use cases by:
Extracting tabular data from a variety of file types, even when the source data is unstructured
Leveraging AI to prepare and map data to a desired schema
Allowing non-technical users to establish validation rules that data must follow in natural language
Enabling end-user collaboration and human-in-the-loop workflows for review where needed
In contrast, ETL handles structured, automated transfers. Once data follows consistent structure and system-aligned formats, ETL tools step in to automate downstream data flows. They run automated jobs between databases, cloud storage, and applications, orchestrating recurring data flows across stable interfaces. Flatfile does not attempt to build connectors, adapters, or orchestrators for these downstream jobs—it’s not a replacement for ETL.
Complementary roles in the data lifecycle
Flatfile
- Prepares external, user-generated files for system use
- Optimized for high variance, one-off or ad hoc formats
- Built for end-user interaction and human review
- Natural language data transformation at the point of intake
- Best for onboarding and migrating customer data
ETL Tools
- Moves structured or semi-structured data between internal systems
- Optimized for low-variance, repeatable jobs
- Built for automation and background execution
- Rule-based transformation for structured data within recurring workflows
- Best for syncing systems, aggregating data, and pipeline orchestration
Flatfile does its job exceptionally well
Flatfile’s purpose is focused and critical: helping teams convert messy, file-based data into clean, structured, and reviewable formats they can trust. It works in concert with your ETL pipeline, with each tool designed for a distinct layer of your data system.