IT Glossary
What is ETL?
Extract, Transform, Load: the pipeline that pulls data from company systems, cleans and unifies it, then pours it into the analytics store.
Between the systems that produce data (ERP, invoicing, e-commerce platform, bank) and the reports that consume it runs an invisible piece of plumbing called ETL — Extract, Transform, Load. Extraction pulls records from the sources through APIs, database connections or files. Transformation does the dirty, decisive work: unifying formats so 'Smith Ltd' and 'SMITH LIMITED' become one customer, applying business rules such as what exactly counts as net revenue, handling gaps and duplicates. Loading deposits the result, ordered, into the data warehouse that dashboards drink from. Why a manager should care: this pipeline — not the pretty charts — is where it gets decided whether the numbers you look at are true. Garbage in, garbage out is the unwritten law of reporting, and ETL is the treatment plant. In practice, any serious BI or warehouse project spends the majority of its effort here, often 60-70 percent; the pipeline runs automatically on schedule — last night's data in the morning's report — and must be monitored, because a pipe that silently clogged on Tuesday leaves the dashboard confidently lying with Monday's figures. When comparing proposals, ask how failures are detected and announced; the answer reveals whether you are buying infrastructure or a demo.
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Why it matters for your business
Numbers you can trust
Systematic cleaning and unification means reports built on reality — not decisions made on duplicates, gaps and diverging definitions.
Manual reporting labour, abolished
The exports, spreadsheet copies and monthly reconciliation of figures get automated once and run alone, every night.
A reusable foundation
Once the pipeline exists, every new report reuses it — the next business question costs days, not projects.
Frequently asked questions
ETL or ELT — what is the difference in vendors' proposals?
The order of operations: ETL transforms data before loading (the classic), ELT loads it raw into modern cloud warehouses and transforms there using their computing power (today's usual, with tools like dbt). For you it is an implementation detail — demand the outcome: correct data, on time, documented.
How often does data refresh through a pipeline?
As the business requires: classically overnight, daily — sufficient for most management reporting; when needed, hourly or near real time via streaming, at rising cost and complexity. The clarifying question: what decision would you take differently with data from five minutes ago instead of this morning?
What happens when a source changes structure and the pipeline breaks?
The reality of every living data pipeline: updated APIs, renamed columns, moved exports. Serious implementations therefore include monitoring with alerts (failure announces itself), validation of incoming data, and a maintenance arrangement — a data pipeline is infrastructure, not a project delivered and forgotten.
Let’s talk about your project
Message us on WhatsApp or send an email — you talk directly to a developer.
office@northdan.com · +40 752 070 247