DATA PIPELINES THAT DON'T WAKE ANALYSTS AT 2AM

Quick Answer: NUUN Digital builds data pipelines on modern ELT patterns — ingestion via Fivetran, Airbyte, or custom connectors; transformation in dbt; reverse ETL via Hightouch or Census. Every pipeline shipped with monitoring, alerting, cost controls, and documented ownership. Pipelines that run themselves don't run your people.

WHAT WE DELIVER

Ingestion pipelines. SaaS, database, file, and event-stream ingestion to warehouse.
Transformation pipelines. dbt-based modelling with tests and documentation.
Reverse ETL pipelines. Warehouse-to-activation (ad platforms, CRM, email, product).
Event streaming. Kafka, Kinesis, Segment, or Snowplow pipelines for real-time use cases.
Observability. Monte Carlo, Elementary, or custom data-quality monitoring.
Cost controls. Warehouse cost monitoring, query optimization, and retention policies.

HOW WE DO IT

Map source-to-consumer. What data, from where, to where, at what freshness SLA.
Choose the pattern. ELT-first for modern warehouses; ETL where regulatory or latency demands.
Build with testing. dbt tests, schema-contract enforcement, and anomaly detection.
Monitor for cost and quality. Both matter; ignoring either creates long-term problems.
Document with lineage. Automatic lineage generation across the pipeline.

PLATFORMS WE WORK WITH

Fivetran · Airbyte · Stitch · Segment · Snowplow · Kafka · Kinesis · dbt · Dataform · Hightouch · Census · Polytomic · Monte Carlo · Elementary · Snowflake · BigQuery · Databricks · Redshift.

SELECTED WORK

Confidential retailer — Fivetran + dbt + Hightouch stack → pipeline run-time [X]× faster; cost [X]% lower. Read case →
Financial services client — Kafka-based event streaming → real-time fraud-signal pipeline. Read case →

SOURCES & FURTHER READING

Data Management practice
Data architecture and modelling
Data governance and quality
dbt — https://www.getdbt.com/
Fivetran — https://www.fivetran.com/

Frequently asked.

Fivetran, Airbyte, or custom?

Fivetran for managed reliability and breadth of connectors. Airbyte for cost-sensitive or open-source environments. Custom for edge cases without existing connectors or for performance-critical paths. Most enterprises use a mix.

ETL or ELT?

ELT is the default for cloud-native warehouses. ETL where data must be transformed before landing (regulatory, sensitive data) or where low-latency transformation is required.

How do you handle data-quality issues in source systems?

Upstream: contract enforcement with source teams. Pipeline: dbt tests at each transformation stage. Monitoring: data-observability tools (Monte Carlo, Elementary) flag issues before they reach consumers.

What about cost? Warehouses can get expensive.

Warehouse cost monitoring and optimization is part of every engagement. Query-level cost attribution, model-materialization review, and retention policy management typically save 20–40% on warehouse spend.

Can you handle real-time pipelines?

Yes. Kafka, Kinesis, Pub/Sub, and Segment event streams for sub-minute latency. Real-time requirements should match real-time use cases; most analytics doesn't need it.