You’ve got your data pipeline all set up. The information that is the lifeblood of your organization is flowing, pumping, moving exactly where it needs to go. Everyone has the insights they need to run your business.
BUT … your data pipeline keeps breaking.
Oops.
How can you fix data pipeline fragility? ELT (and Extract) to the rescue.
Key takeaways
- Traditional ETL pipelines are fragile and tightly coupled.
- Schema changes and upstream issues often break pipelines.
- ELT decouples transformation, improving resilience.
- Automated connectors reduce maintenance and errors.
- Extract offers built-in observability and error recovery.
- ELT enables scalable, cloud-native data workflows.
Why data pipelines break
Let’s start at the beginning.
Why do data pipelines break?
There are dozens of reasons why data pipelines fail all at different stages of traditional ETL processes. Most of them have to do with the fact that traditional ETL pipelines are brittle because they are tightly coupled at every stage.
In the extract phase there are at least 4 key drivers of pipeline fragility:
- Source schema changes
- Upstream data availability issues
- Data volume spikes
- Data quality issues
In traditional ETL systems, source system schema changes are dangerous. ETL pipelines that expect 1 thing but get another can barf, as an unexpected new column or field name breaks the extraction code. Upstream data availability issues, when files or extracts aren’t available as expected, or simple network outages, can also cause problems.
Data volume spikes can overload extraction processes, perhaps pushing memory constraints too far or simply taking too long at existing network speeds. And bad data quality can break data pipelines if your ETL system encounters corrupt or malformatted records.
Real-time data pipelines are particularly vulnerable, especially if events arrive out of expected order.
In the transform phase, key challenges include:
- Bugs or too-rigid logic
- Schema mismatches
- Resource constraints
- Sequential dependencies
Your transformations might use complex custom scripts. Any upstream changes could throw them off, halting your entire pipeline. Or a schema mismatch that made it through the extract phase might not calculate properly.
Resource constraints could hit your transform stage as well. Heavy CPU or memory usage might exceed resources, causing jobs to crash or slow down dramatically.
Plus, ETL transforms often have chained dependencies. If any initial or intermediate step fails, downstream code breaks. One broken transformation can stop the entire pipeline in some circumstances, and partial results often aren’t usable … meaning you need to do a full rerun.
Finally, in the load phase, you might find data pipeline issues in traditional ETL systems thanks to:
- Target schema changes or constraints
- Partial load failures
- Lack of idempotence (re-run safety)
- Poor error handling
Target systems can evolve just like sources, and that can cause failures in ETL systems. Partial loads can lead to inconsistent data, requiring painful manual and error-prone corrections. Traditional load processes that aren’t safe to rerun make recovery hard, and poor error handling (especially with the lack of proper alerts) make finding problems difficult.
ELT and Extract can help fix data pipeline fragility
You can significantly improve pipeline fragility with a modern ELT set-up in Extract.
There’s essentially 3 core reasons why ELT improves reliability, is more resilient to change, and offers simpler maintenance:
- Automated and resilient data extraction
- Transformation shifts downstream
- Improved observability and lower maintenance
Thanks to managed connectors in ELT tools like Extract, you eliminate custom extraction scripts, removing a whole layer of fragility, maintenance, and potential human error. We continually update connectors to adapt to new API changes or data source variations, meaning you don’t have to.
Also, Extract can automatically detect and adapt to schema changes, meaning you have built-in flexibility for new fields or columns in data sources. The pipeline doesn’t break: and Extract offers a full audit log so you can see exactly what changed.
Because ELT offers incremental updates that don’t require a full reload every time something small changes, there’s also less risk of a major batch operation going bad.
When transformation shifts downstream, you de-risk and your data pipeline becomes more resilient. ELT ensures you at least have the latest raw data available, and you can fix transformation logic without losing or delaying ingestion. And, of course, by shifting transform stages downstream, you can leverage the power of big modern cloud data warehouses … solving data volume and spiking issues in a scalable space.
There’s also much faster iteration and less fragile logic in ELT. Transformations are defined in SQL or dbt, and they’re easier to modify and deploy in response to change … no new extract logic to build.
Finally, there’s improved observability and lower maintenance because the process is significantly less manual: connectors are hands-off, updated regularly, and fixes happen behind the scenes. Extract also has best-in-class logging so you can easily see what’s happening: everything is observable.
ELT tools, like Extract, allow pipeline designs that bend instead of breaking when requirements change.
How to quickly compare the 2 approaches
| Issue | ETL | ELT (Extract) |
| Schema change handling | Brittle Upstream schema changes can break the pipeline unless manually addressed. | Adaptable Connectors auto-detect schema changes and propagate them. New fields added to the destination seamlessly. |
| Upstream dependency | Fragile Depends on source availability and timing. Limited built-in retry. | Resilient Built-in retries and scheduling handle intermittent failures. Pipelines can pause & resume from checkpoints. |
| Transformation coupling | Tightly coupled Transformation is in-line. If the transform step fails, no data is loaded. Changes require redesign. | Decoupled Raw data is loaded first; a transform error does not stop ingestion. Transformations can be fixed downstream. |
| Error handling & recovery | Limited Failures might require manual cleanup or rerun. No automatic resume. Data inconsistencies possible. | Robust Automated error handling minimizes issues. |
| Monitoring & alerts | Ad-hoc Monitoring usually relies on external schedulers or custom scripts. Issues can go unnoticed. | Integrated Comprehensive monitoring and instant alerts are built in. Pipelines have observable metrics. Teams get notified of failures or anomalies in real-time. |
| Maintenance overhead | High Pipelines are bespoke and require skilled engineers to manage changes. | Low Largely automated and off-the-shelf. Less custom code means fewer bugs. |
| Scaling & performance | Resource-bound Performance limited by server capacity. Large volumes can cause failures or slowdowns. | Cloud-scaled Leverages cloud data warehouse and managed infrastructure for scaling. Fewer crashes due to load. |
ELT and Extract: safe, reliable, scalable data pipelines
ELT tools improve schema flexibility, error handling, monitoring, and reduce the manual fragility present in ETL pipelines. Batch and streaming ELT data pipelines tend to be brittle, tightly coupled, with rigid schemas.
With ELT, you’re far less likely to break your data pipeline (or have it broken by others). And with Extract, you’re doing it in a tool that is incredibly efficient and scalable.