The Hidden Costs of “Grab Everything” Data Pipelines
How performance, cloud costs, and compliance risks creep in when ETL/ELT lacks discipline.
In many organizations, the data team’s first instinct is to grab everything. Full-table extracts, every column, every row, all history-dumped straight into the cloud warehouse. The thinking is: “Better to have it all now than get blocked later.” There’s a good argument that grabbing everything helps “future-proof” the extract and to create that “bronze layer” strategy.
It feels safe. It feels fast.
Why “Grab Everything” can be a Problem
Performance hits on source systems
Extracting entire datasets strains production systems and can slow down applications for end users. This is especially risky when queries run during business hours.Cloud cost blow-ups
Warehouses balloon with unused data. Processing costs increase as pipelines churn through irrelevant columns and rows.Security and compliance blind spots
By pulling everything, data teams may inadvertently ingest PII (tax IDs, SSNs, emails, phone numbers, etc.). Even if they try to filter out obvious fields, applications often allow users to enter personal info into free-text or custom fields. That means sensitive data may be lurking where you don’t expect it.False sense of speed
Bulk extraction only delays the hard work. At some point, the data team must learn what the data means, how it’s structured, and which pieces matter. Until then, analysts and business stakeholders are sifting through noise.
The Minimum Standard: Learn What Not to Take
At the very least, data teams need to spend time understanding what shouldn’t be extracted. This isn’t something you can automate away by filtering column names like taxId or ssn.
Custom fields – Many systems let end users create or populate freehand fields that may contain personal information. Without talking to developers, data engineers may not even know these fields exist.
Hidden PII – Emails stored in description fields, IDs tucked into notes, or even full addresses in “comments” fields.
Business logic fields – Certain columns may only be relevant for transactional workflows and add no analytical value. Pulling them just adds clutter.
Working with the development team is the only way to uncover these blind spots. They know where custom fields live, how user data is handled, and which parts of the schema are critical versus incidental.
A Smarter Approach
While there are legitimate cases where broad extraction makes sense-like when building a data lake for exploratory analytics or when dealing with rapidly changing schemas - "grabbing everything" should not be the default approach. Even when you consider it as a temporary measure under delivery pressure, it should not be the end state.
A smarter path is:
Spend time upfront mapping what matters.
Collaborate with developers to understand system behaviors and data entry quirks.
Write extracts that are selective, performant, and respectful of source systems.
Treat PII seriously, assuming it may appear in unexpected places.
This takes more effort at the start - but it pays dividends in lower costs, cleaner datasets, and fewer compliance risks down the road.
Final Thought
ETL/ELT should be about intentionality, not volume. You don’t always need everything - you need the right things. And that starts by learning what not to take.