ETL pipelines you can rerun without duplicating the data
Most ETL breakage comes from the same three places: a rerun that double-loads rows, a source that changed shape without warning, and a partial failure that leaves half a batch applied. We build pipelines that are safe to rerun by design, that detect schema drift and stop rather than guess, and that reconcile counts against the source so a silent shortfall is caught the same day it happens.
You probably need this if
- Rerunning the job duplicated everything
- One bad row killed the whole batch
- Nothing reconciles against the source
- The transformation logic is undocumented
Where these projects go wrong
Four failure modes we see repeatedly. Knowing them up front is most of the battle.
- 01
Rerunning the job duplicated everything
A load failed halfway, someone ran it again, and the table now holds two copies of a day's transactions with no natural key to deduplicate on.
- 02
One bad row killed the whole batch
A malformed date in a single record aborted a two-hour load. There is no quarantine table, so nothing arrived and the whole run has to start again.
- 03
Nothing reconciles against the source
The source system has 84,000 orders and the warehouse has 83,140. Nobody noticed for a month because no count comparison ever runs.
- 04
The transformation logic is undocumented
A currency conversion and a timezone shift are buried inside a 900-line stored procedure written by someone who left, and no one is confident enough to change it.
- Source-to-target field mapping document
- Idempotent, safely rerunnable load logic
- Incremental extraction with watermarking
- Cleaning, deduplication and type coercion rules
- Quarantine table for rejected records
- Schema drift detection with alerting
- Row-count and checksum reconciliation reports
- Scheduling, retry and backfill procedures
The engagement, step by step
- 01
Map fields and agree the rules
Every target field traced to its source, with the handling for nulls, duplicates and conflicts written down. Ambiguity here becomes a data quality incident later.
- 02
Make every load rerunnable
Merge on a stable key rather than appending, so a rerun after a failure corrects the data instead of duplicating it. This single decision prevents most ETL emergencies.
- 03
Quarantine instead of aborting
Bad records go to a rejects table with the reason attached and the rest of the batch completes, so one malformed row cannot hold up a day of data.
- 04
Reconcile after every run
Source and target counts and totals compared automatically, with a mismatch raising an alert rather than waiting for someone to spot it in a report.
Benefits & business outcomes
What changes when this work is done properly — not activity metrics, business results.
- 01
Reruns that fix, not duplicate
Idempotent loads merge on a stable key, so recovering from a half-failed batch corrects the warehouse instead of doubling yesterday's transactions.
- 02
Bad rows quarantined, good rows through
One malformed date no longer aborts a two-hour job. Rejects land with a reason; the rest of the batch completes and the exception queue is visible the same day.
- 03
Reconciliation you can show finance
Source and target counts and checksums compared after every run — mismatches alert before a month of silent shortfall reaches a board pack.
- 04
Schema drift that stops guessing
Pipelines detect column adds, renames and type changes and halt rather than nulling a field three reports depend on.
The stack for this work
- Python
- dbt
- Airflow
- Airbyte
- PostgreSQL
- BigQuery
- Snowflake
- SQL
- Pandas
- Companies syncing data between disconnected systems
- Teams migrating off a legacy database
- Businesses whose nightly loads fail unpredictably
- Organisations consolidating data for reporting
Working remotely with teams in United States, United Kingdom, Canada, Europe, United Arab Emirates, Saudi Arabia, Singapore, Australia, New Zealand and India.
Industries we serve
The same engineering patterns apply across sectors — the domain language and constraints change.
- Retail & eCommerce
- Banking & fintech
- Insurance
- Manufacturing
- Healthcare providers
- Travel & hospitality
- Telecom
- Public sector reporting
Why choose BuildsInfinity
Practical reasons teams choose BuildsInfinity for etl development services — grounded in how we actually work, not a slogan list.
- 01
Managed connectors where they exist
We recommend Fivetran-class tools for Stripe, HubSpot and common databases. Custom ETL development is reserved for legacy sources and business-specific transforms — not rebuilding what already ships.
- 02
ELT as the default on modern warehouses
Raw land first, transform in the warehouse — so metric definitions can change without re-extracting history the source may no longer hold.
- 03
Mapping documents before code
Every target field traced to its source with null and conflict rules written down. Ambiguity here becomes a data quality incident later, and we refuse to skip it.
Related services
Data Engineering Services
Ingestion, modelling, orchestration and quality checks — pipelines that alert when they break rather than serving stale numbers.
Data Warehouse Development
Dimensional modelling, history that survives source overwrites, incremental loads and query costs that stay predictable.
Business Intelligence Services
Reporting people actually open — defined metrics, decision-led dashboards, self-serve access and alerts instead of daily checking.
Need etl development services?
Send a paragraph about the problem and you will get a considered reply from an engineer — a shape, a rough timeline and an honest view on whether we are the right fit. Request a consultation or project estimate anytime.