Insight Analytics
Service

ETL Development

Pipelines that load incrementally, test themselves, and tell you before your users notice something is wrong.

An ETL pipeline is only as valuable as your confidence in it. Plenty of companies have pipelines that run — and a quiet habit of checking the numbers manually anyway, because nobody trusts the load actually completed.

We build pipelines with the trust layer included: incremental loads with watermarks, row-count and referential tests at each stage, quarantine tables for records that fail validation, and alerts that fire on staleness as well as errors.

Whether the target is a Fabric lakehouse, a Snowflake warehouse, or a SQL Server data mart, the shape is the same: land raw, transform in stages you can inspect, publish only what passes.

Problems this solves

What we usually walk into

  • Loads fail overnight and the dashboard silently shows yesterday's data.
  • Full reloads take so long they can't run during the business day.
  • Bad records break the load instead of being quarantined.
  • There's no way to tell when a table was last successfully refreshed.

Technologies

What we build with

Microsoft FabricAzure Data FactorydbtAirflowSnowflakeDatabricks

Industries that benefit

  • Retail
  • Healthcare
  • Financial services
  • Manufacturing
  • Education

Implementation process

How the engagement runs

  1. 01

    Source profiling

    Understand volume, change patterns, keys, and how each source behaves when it misbehaves.

  2. 02

    Landing and staging design

    Raw landing preserved for replay, staging for typing and cleansing.

  3. 03

    Incremental logic

    Watermarks or change data capture so loads take minutes, not hours.

  4. 04

    Data quality tests

    Uniqueness, not-null, referential integrity, and freshness checks that gate publication.

  5. 05

    Observability

    Run logs, lineage, and alerting wired into the channels your team already watches.

Sample screens

What the finished work looks like

Representative layouts using demonstration data — client work is never shown without written permission.

Pipeline health

Tables

184

Fresh

100%

Failures

0

Pipeline health

Load status and freshness for every table in the warehouse.

Load volumes

Rows/day

10.2M

Avg load

7 min

Sources

14

Load volumes

Rows loaded per source with incremental versus full comparison.

Quality test results

Tests

312

Pass

99.7%

Quarantine

418

Quality test results

Passing, warning, and quarantined records by rule.

Illustrative example

How a etl development engagement typically plays out

Anonymised scenario · not a verified client record

Multi-brand retailer, 60 stores

Challenge

Nightly loads from the POS, e-commerce platform, and inventory system frequently failed, and the merchandising team had learned to check row counts by hand each morning before trusting the report.

Solution

Rebuilt the ingestion in Azure Data Factory with incremental watermarks, added a dbt-tested curated layer with freshness checks, and quarantined malformed POS records instead of failing the batch.

Result

The morning manual check was retired after two months of clean runs, and merchandising now has store-level data by 5am.

Illustrative figures

99.8%

Load success rate

3.5h → 22 min

Nightly batch

5am

Data ready

This is a composite illustration of the scope, approach, and range of results this service is designed to deliver. It does not describe a specific named client, and the figures are demonstration values rather than audited outcomes. We're happy to talk through real references under NDA on a call.

Deliverables

What you receive

  • Orchestrated ingestion pipelines per source
  • Staging and curated layer models
  • Automated data quality test suite
  • Failure and freshness alerting
  • Lineage documentation and runbook

FAQs

Questions we get asked

ETL or ELT?
ELT into a modern warehouse in most cases — it's cheaper to reprocess and easier to audit. ETL still makes sense when compliance limits what can land raw.
How do you handle schema changes at the source?
Raw landing tolerates new columns, and tests fail loudly on breaking changes rather than corrupting downstream tables.
Can you migrate existing SSIS packages?
Yes. We usually port the logic to Data Factory or dbt rather than lift-and-shift, and validate outputs against the old packages.
What refresh frequency is realistic?
Hourly is straightforward for most sources, near-real-time where the source supports CDC or events.

Related services

Often scoped alongside this

Data Engineering

Lakehouse, streaming, orchestration, and observability.

View data engineering

Data Warehousing

Dimensional models on Snowflake, Fabric, SQL Server, and Azure.

View data warehousing

SQL Development

Enterprise-grade SQL, stored procedures, tuning, and modeling.

View sql development

Talk through your ETL Development project

A 30-minute call is usually enough to tell you whether this is a two-week fix or a two-month build — and roughly what it costs.

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