Clear, owned outputs.
- Pipeline architecture
- Implemented pipelines for agreed sources
- Orchestration and scheduling
- Data quality tests
- Monitoring dashboards and runbooks
ETL and ELT pipelines extract data from source systems, load it into a central platform and transform it for use. Reliable pipelines are incremental, tested, monitored and easy to rerun. We build pipelines that handle real-world mess — late data, schema changes, duplicates — and tell you when something goes wrong.
The problems ETL & ELT Development solves.
Batch, incremental and CDC pipelines with orchestration, tests and monitoring.
Pipelines fail silently and reports show stale data.
Full reloads take hours and strain source systems.
Schema changes in source systems break everything downstream.
Pipeline logic lives in one person's head.
What ETL & ELT Development covers.
Pipeline design
Choosing ETL or ELT and the right tools for each source.
Incremental loading
Loading only new and changed data efficiently.
Change data capture
Streaming changes from databases in near real time.
Orchestration
Scheduling, dependencies, retries and backfills.
Testing and quality
Schema, freshness and business-rule tests.
Monitoring and alerting
Alerts for failures, delays and anomalies.
ETL or ELT: where the transformation happens.
Both move data from sources to a target and reshape it on the way. The difference is where the reshaping runs, and it changes cost, speed and who can maintain it.
| Approach | How it works | Suits |
|---|---|---|
| ETL | Data is transformed in a separate engine before it is loaded into the target. | Strict data-protection rules, legacy targets with little compute, or heavy cleansing before anything lands. |
| ELT | Raw data is loaded first, then transformed inside the warehouse or lakehouse with SQL or Spark. | Cloud platforms with elastic compute; teams that want transformations in version-controlled SQL (for example dbt). |
| Streaming | Events are processed continuously as they arrive, with seconds of latency. | Fraud checks, live inventory, operational dashboards — where hourly batches are too slow. |
Why it's worth it.
Fresh, reliable data
Reports update on time with data you can trust.
Lower load on sources
Incremental and CDC approaches reduce strain.
Maintainability
Documented, tested pipelines anyone on the team can support.
When ETL & ELT Development is the right move.
- Moving data from ERP, CRM and SaaS tools into a warehouse.
- Replacing fragile scripts with orchestrated pipelines.
- Near-real-time data for operational dashboards.
Technologies & platforms
- Azure Data Factory
- Apache Airflow
- dbt
- Fivetran
- Airbyte
- Debezium
- Apache Kafka
- AWS Glue
How ETL & ELT Development work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
ETL & ELT Development questions.
What's the difference between ETL and ELT?
ETL transforms data before loading it; ELT loads raw data first and transforms it inside the platform. ELT suits modern cloud warehouses; ETL is still useful when data must be cleaned or filtered before loading.
Should we buy connectors or build pipelines?
Managed connectors save time for common SaaS sources; custom pipelines suit unusual sources or strict requirements. Most platforms use a mix.
How do you handle schema changes?
With schema detection, contracts for critical sources, alerts on changes and transformations designed to fail clearly rather than silently.
Talk to us about etl & elt development.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.